databases to conduct a literature review

Literature Reviews & Search Strategies

• Defining the Literature Review
• Types of Literature Reviews

Use Multiple Databases

• Search Strategies
• Organizing Your Literature
• Books: Research Design & Scholarly Writing
• Recommended Tutorials

While not every literature search you undertake will be for a systematic review, the Cochrane Handbook's statement that "a search of MEDLINE alone is not considered adequate" holds true for almost all literature reviews. You need to go beyond one database to get a more comprehensive picture of your topic and to minimize selection bias. 

There are A LOT of databases that you could potential search for academic/scholarly articles to use in your literature review. We recommend focusing on resources that specializes in academic sources (ie databases), rather than a general search tool like Google because a lot of scholarly literature is still not discoverable on the open web and when it is you'll often hit a paywall and have to head to a subscription database available through the library to read the full article any way.

All our databases are listed on the A-Z Databases List , these are a few, often recommended, examples:

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Systematic Reviews: Medical Literature Databases to search

• Types of literature review, methods, & resources
• Protocol and registration
• Search strategy
• Medical Literature Databases to search
• Study selection and appraisal
• Data Extraction/Coding/Study characteristics/Results
• Reporting the quality/risk of bias
• Manage citations using RefWorks This link opens in a new window
• GW Box file storage for PDF's This link opens in a new window

How to document your literature search

You should always  document how you have searched each database, what keywords or index terms were used, the date on which the search was performed, how many results you retrieved, and if you use RefWorks to deduplicate results record how many were removed as duplicates and the final number of discrete studies you subjected to your first sift through of study selection.  Here is an example of how to document a literature search on an Excel spreadsheet , this example records a search of the hematology literature for articles about sickle cell disease. Here is another example of  how to document a literature search, this time on one page of a Word document , this example records a search of the medical literature for a poster on Emergency Department throughput.  The numbers recorded can then be used to populate the PRISMA flow diagram summarizing the literature search.

In the final report add as an appendix the full electronic search strategy for each database searched for the literature review e.g. MEDLINE with MeSH terms, keywords & limits

In the final report in the methods section:

PRISMA checklist Item 7 information sources will be reported as:

• What databases/websites you searched, the name of the database search platform and the start/end dates the index covers if relevant e.g. OVID MEDLINE (1950-present, or just PubMed
• Who developed & conducted the searches
• Date each database/website was last searched
• Supplementary sources - what other websites did you search? What journal titles were hand searched, whether reference lists were checked, what trial registries or regulatory agency websites were searched, were manufacturers or other authors contacted to obtain unpublished or missing information on study methods or results.

PRISMA checklist Item 8 search will be reported as:

• In text: describe the principal keywords used to search databases, websites & trials registers

What databases/indexes should you search?

At a minimum you need to search MEDLINE ,  EMBASE , and the  Cochrane CENTRAL  trials register .  This is the recommendation of three medical and public health research organizations: the U.S.  Agency for Healthcare Research and Quality ( AHRQ ), the U.K. Centre for Reviews and Dissemination ( CRD ), and the International Cochrane Collaboration (Source:  Institute of Medicine (2011) Finding What Works in Healthcare: Standards for Systematic Reviews  Table E-1, page 267).  Some databases have an alternate version, linked in parentheses below, that search the same records sets, ie the content of MEDLINE is in PubMed and Scopus, while the content of EMBASE is in Scopus. You should reformat your search for each database as appropriate, contact your librarian if you want help on how to search each database.  

Begin by searching:

1.        MEDLINE  (or  PubMed )

2.       EMBASE (or  Scopus )  Please note Himmelfarb Library does not have a subscription to EMBASE. The content is in the Scopus  database that you can search using keywords, but it is not possible to perform an EMTREE theasaurus search in Scopus.

3.        Cochrane Central Trials Register  (or  Cochrane Library ). In addition Cochrane researchers recommend you search the clinicaltrials.gov and ICTRP clinical trial registries due to the low sensitivity of the Cochrane CENTRAL index because according to Hunter et al (2022) "register records as they appear in CENTRAL are less comprehensive than the original register entry, and thus are at a greater risk than other systems of being missed in a search."

The Polyglot Search Translator is a very useful tool for translating search strings from PubMed or Medline via Ovid across multiple databases, developed by the Institute for Evidence-Based Healthcare at Bond University. But please note Polyglot does not automatically map subject terms across databases (e.g. MeSH terms to Emtree terms) so you will need to manually edit the search syntax in a text editor to change to the actual subject terms used by another database.

The Yale Mesh Analyzer is another very useful tool you can copy and paste in a list of up to 20 PMID numbers for records in the PubMed database, the Yale Mesh Analyzer will then display the Mesh Medical Subject Headings for those 20 articles as a table so you can identify and compare what Mesh headings they have in common, this can suggest additional search terms for your PubMed search.

The MedSyntax tool is another useful tool, for parsing out very long searches with many levels of brackets. This would be useful if you are trying to edit a pre-existing search strategy with many levels of parentheses.

Some sources for pre-existing database search filters or "hedges" include:

• CADTH Search Filters Database ,
• McMaster University Health Information Research Unit ,
• University of York Centre for Reviews and Dissemination InterTASC Information Specialists' Sub-Group ,
• InterTASC Population Specific search filters  (particularly useful for identifying Latinx, Indigenous people's, LGBTQ, Black & Minority ethnic)
• CareSearch Palliative Care PubMed search filters  (bereavement, dementia, heart failure, lung cancer, cost of care, and Palliative Care)
• Low and Middle Income countries filter at https://epoc.cochrane.org/lmic-filters . 
• Search Pubmed for another validated search filter using some variation of a search like this, possibly adding your discipline or search topic keywords: ("Databases, Bibliographic"[Mesh] OR "Search Engine"[Mesh]) AND ("Reproducibility of Results"[Mesh] OR "Sensitivity and Specificity"[Mesh] OR validat*) AND (filter OR hedge) .
• Search MEDLINE (or PubMed), preferably using a peer reviewed search strategy per protocol and apply any relevant methodology filters.
• Search EMBASE (or Scopus) and the Cochrane Central trials register using appropriately reformatted search versions for those databases, and any other online resources. 
• You should also search other subject specific databases that index the literature in your field.  Use our Himmelfarb Library  research guides  to identify other  subject specific databases . 
• Save citations in Covidence to deduplicate citations prior to screening.
• After screening export citations to  RefWorks database when you are ready to write up your manuscript. The Covidence and Refworks databases should be shared with all members of the investigative team.

Supplementary resources to search

Other member of your investigative team may have ideas about databases, websites, and journals they think you should search. Searching these sources is not required to perform a systematic review. You may need to reformat your search keywords.

Researchers at GW should check our subject research guides for suggestions, or check the libguides community for a guide on your subject.

In addition you may wish to search one or more of the following resources:

• Google Scholar
• BASE  academic search engine is useful for searching in University Institutional Repositories
• Cochrane Database of Systematic Reviews  to search for a pre-existing systematic review on your topic
• Epistemonikos database, has a matrix of evidence table so you can see what citations are shared in common across existing systematic reviews of the same topic. This feature might help identify sentinel or 'don't miss' articles.

You might also consider searching one or more of the following websites depending on your topic:

Clinical trial registers. The Cochrane Collaboration recommends for a systematic review to search both clinicaltrials.gov and the WHO ICTRP (See http://handbook.cochrane.org/ section 4.3):

• ClinicalTrials.gov  - also contains study population characteristics and results data of FDA regulated drugs and medical devices in NIH funded studies produced after January 18, 2017.
• WHO ICTRP  - trials register
• TRIP  - searchable index of clinical trials, guidelines,and regulatory guidance
• CenterWatch
• Current Controlled Trials
• European Clinical Trials Register
• ISRCTN Register
• COMPARE - tracks outcome switching in clinical trials
• OpenTrials - aims to match published trials with the underlying data where this is publicly available in an open source 
• ECRI Guidelines Trust

Grey literature resources:

• WONDER - CDC data and reports
• FDSys - search federal government publications
• Science.gov
• NRR Archive
• NIH Reporter
• re3data registry of data repositories
• Data Repositories (listed by the Simmons Open Access Directory)
• OpenDOAR  search academic open access research repositories
• f1000research search open access repositories of articles, slides, and research posters, in the life sciences, public health, education, and communication.
• RAND Health Reports
• National Academy of Medicine Publications
• Kaiser Family Foundation 
• Robert Wood Johnson Foundation health and medical care data archive
• Milbank Memorial Fund reports and issue briefs
• Also search the resources listed in the CADTH (2019) Grey Matters checklist.

Preprints 

• See our Himmelfarb preprints guide page on finding preprints , a useful database for searching Health Sciences preprints is  Europe PMC

Dissertations and Theses:

• Proquest Dissertations and Theses Online 
• Networked Digital Library of Theses and Dissertations
• Open Access Theses and Dissertations
• WorldCat and change Content: from Any Content to Thesis/dissertations

Conference proceedings:

Most conference proceedings are difficult to find because they may or may not be published. Only select individual papers may be made available in print as a book, journal, or series, rather than all of the presented items. Societies and Associations may only publish abstracts, or extended abstracts, from a conference, often in an annual supplement to an issue of the journal of record of that professional society.  Often posters are not published, if they are they may be made available only to other conference registrants at that meeting or online. Authors may "publish" their conference papers or posters on personal or institutional websites.  A limited set of conference proceedings databases include the following:

• BASE  academic search engine, has an Advanced Search feature with a Limit by Type to 'Conference Objects', this is useful for searching for conference posters and submissions stored in University Institutional Repositories.
• Web of Science - click All Databases and select Core Collection - under More Settings limit to the Conference Proceedings Citation Index (CPCI) - searches a limited set of conferences on Science, Social Science and Humanities from 1990-present.
• Scopus - Limit Document Type to Conference Paper or Conference Review.
• Proquest  - Limit search results to conference papers &/or proceedings under Advanced Search.
• BioMed Central Proceedings  - searches a limited set of biomedical conference proceedings, including bioinformatics, genetics, medical students, and data visualization.
• F1000 Research - browse by subject and click the tabs for articles, posters, and slides - which searches a limited number of biology and medical society meetings/conferences. This is a voluntary self-archive repository.

Individual Journals 

• You may choose to "hand search" select journals where the research team reads the Table of Contents of each issue for a chosen period of time.  You can look for the names of high impact journal titles in a particular field indexed in Journal Citation Reports  (JCR). Please note as of August 2021 ISI are linking to a new version of JCR that currently does not have the particularly helpful 'Browse by Category' link working, so I recommend you click the Products link in the top right corner and select Journal Citation Reports (Classic) to switch back to the old version to get that functionality back.
• The AllTrials petition aims to motivate health care researchers to petition regulators and research bodies to require the results and data of all clinical trials be published.
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• Open access
• Published: 06 December 2017

Optimal database combinations for literature searches in systematic reviews: a prospective exploratory study

• Wichor M. Bramer 1 ,
• Melissa L. Rethlefsen 2 ,
• Jos Kleijnen 3 , 4 &
• Oscar H. Franco 5  

Systematic Reviews volume  6 , Article number:  245 ( 2017 ) Cite this article

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Within systematic reviews, when searching for relevant references, it is advisable to use multiple databases. However, searching databases is laborious and time-consuming, as syntax of search strategies are database specific. We aimed to determine the optimal combination of databases needed to conduct efficient searches in systematic reviews and whether the current practice in published reviews is appropriate. While previous studies determined the coverage of databases, we analyzed the actual retrieval from the original searches for systematic reviews.

Since May 2013, the first author prospectively recorded results from systematic review searches that he performed at his institution. PubMed was used to identify systematic reviews published using our search strategy results. For each published systematic review, we extracted the references of the included studies. Using the prospectively recorded results and the studies included in the publications, we calculated recall, precision, and number needed to read for single databases and databases in combination. We assessed the frequency at which databases and combinations would achieve varying levels of recall (i.e., 95%). For a sample of 200 recently published systematic reviews, we calculated how many had used enough databases to ensure 95% recall.

A total of 58 published systematic reviews were included, totaling 1746 relevant references identified by our database searches, while 84 included references had been retrieved by other search methods. Sixteen percent of the included references (291 articles) were only found in a single database; Embase produced the most unique references ( n  = 132). The combination of Embase, MEDLINE, Web of Science Core Collection, and Google Scholar performed best, achieving an overall recall of 98.3 and 100% recall in 72% of systematic reviews. We estimate that 60% of published systematic reviews do not retrieve 95% of all available relevant references as many fail to search important databases. Other specialized databases, such as CINAHL or PsycINFO, add unique references to some reviews where the topic of the review is related to the focus of the database.

Conclusions

Optimal searches in systematic reviews should search at least Embase, MEDLINE, Web of Science, and Google Scholar as a minimum requirement to guarantee adequate and efficient coverage.

Peer Review reports

Investigators and information specialists searching for relevant references for a systematic review (SR) are generally advised to search multiple databases and to use additional methods to be able to adequately identify all literature related to the topic of interest [ 1 , 2 , 3 , 4 , 5 , 6 ]. The Cochrane Handbook, for example, recommends the use of at least MEDLINE and Cochrane Central and, when available, Embase for identifying reports of randomized controlled trials [ 7 ]. There are disadvantages to using multiple databases. It is laborious for searchers to translate a search strategy into multiple interfaces and search syntaxes, as field codes and proximity operators differ between interfaces. Differences in thesaurus terms between databases add another significant burden for translation. Furthermore, it is time-consuming for reviewers who have to screen more, and likely irrelevant, titles and abstracts. Lastly, access to databases is often limited and only available on subscription basis.

Previous studies have investigated the added value of different databases on different topics [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Some concluded that searching only one database can be sufficient as searching other databases has no effect on the outcome [ 16 , 17 ]. Nevertheless others have concluded that a single database is not sufficient to retrieve all references for systematic reviews [ 18 , 19 ]. Most articles on this topic draw their conclusions based on the coverage of databases [ 14 ]. A recent paper tried to find an acceptable number needed to read for adding an additional database; sadly, however, no true conclusion could be drawn [ 20 ]. However, whether an article is present in a database may not translate to being found by a search in that database. Because of this major limitation, the question of which databases are necessary to retrieve all relevant references for a systematic review remains unanswered. Therefore, we research the probability that single or various combinations of databases retrieve the most relevant references in a systematic review by studying actual retrieval in various databases.

The aim of our research is to determine the combination of databases needed for systematic review searches to provide efficient results (i.e., to minimize the burden for the investigators without reducing the validity of the research by missing relevant references). A secondary aim is to investigate the current practice of databases searched for published reviews. Are included references being missed because the review authors failed to search a certain database?

Development of search strategies

At Erasmus MC, search strategies for systematic reviews are often designed via a librarian-mediated search service. The information specialists of Erasmus MC developed an efficient method that helps them perform searches in many databases in a much shorter time than other methods. This method of literature searching and a pragmatic evaluation thereof are published in separate journal articles [ 21 , 22 ]. In short, the method consists of an efficient way to combine thesaurus terms and title/abstract terms into a single line search strategy. This search is then optimized. Articles that are indexed with a set of identified thesaurus terms, but do not contain the current search terms in title or abstract, are screened to discover potential new terms. New candidate terms are added to the basic search and evaluated. Once optimal recall is achieved, macros are used to translate the search syntaxes between databases, though manual adaptation of the thesaurus terms is still necessary.

Review projects at Erasmus MC cover a wide range of medical topics, from therapeutic effectiveness and diagnostic accuracy to ethics and public health. In general, searches are developed in MEDLINE in Ovid (Ovid MEDLINE® In-Process & Other Non-Indexed Citations, Ovid MEDLINE® Daily and Ovid MEDLINE®, from 1946); Embase.com (searching both Embase and MEDLINE records, with full coverage including Embase Classic); the Cochrane Central Register of Controlled Trials (CENTRAL) via the Wiley Interface; Web of Science Core Collection (hereafter called Web of Science); PubMed restricting to records in the subset “as supplied by publisher” to find references that not yet indexed in MEDLINE (using the syntax publisher [sb]); and Google Scholar. In general, we use the first 200 references as sorted in the relevance ranking of Google Scholar. When the number of references from other databases was low, we expected the total number of potential relevant references to be low. In this case, the number of hits from Google Scholar was limited to 100. When the overall number of hits was low, we additionally searched Scopus, and when appropriate for the topic, we included CINAHL (EBSCOhost), PsycINFO (Ovid), and SportDiscus (EBSCOhost) in our search.

Beginning in May 2013, the number of records retrieved from each search for each database was recorded at the moment of searching. The complete results from all databases used for each of the systematic reviews were imported into a unique EndNote library upon search completion and saved without deduplication for this research. The researchers that requested the search received a deduplicated EndNote file from which they selected the references relevant for inclusion in their systematic review. All searches in this study were developed and executed by W.M.B.

Determining relevant references of published reviews

We searched PubMed in July 2016 for all reviews published since 2014 where first authors were affiliated to Erasmus MC, Rotterdam, the Netherlands, and matched those with search registrations performed by the medical library of Erasmus MC. This search was used in earlier research [ 21 ]. Published reviews were included if the search strategies and results had been documented at the time of the last update and if, at minimum, the databases Embase, MEDLINE, Cochrane CENTRAL, Web of Science, and Google Scholar had been used in the review. From the published journal article, we extracted the list of final included references. We documented the department of the first author. To categorize the types of patient/population and intervention, we identified broad MeSH terms relating to the most important disease and intervention discussed in the article. We copied from the MeSH tree the top MeSH term directly below the disease category or, in to case of the intervention, directly below the therapeutics MeSH term. We selected the domain from a pre-defined set of broad domains, including therapy, etiology, epidemiology, diagnosis, management, and prognosis. Lastly, we checked whether the reviews described limiting their included references to a particular study design.

To identify whether our searches had found the included references, and if so, from which database(s) that citation was retrieved, each included reference was located in the original corresponding EndNote library using the first author name combined with the publication year as a search term for each specific relevant publication. If this resulted in extraneous results, the search was subsequently limited using a distinct part of the title or a second author name. Based on the record numbers of the search results in EndNote, we determined from which database these references came. If an included reference was not found in the EndNote file, we presumed the authors used an alternative method of identifying the reference (e.g., examining cited references, contacting prominent authors, or searching gray literature), and we did not include it in our analysis.

Data analysis

We determined the databases that contributed most to the reviews by the number of unique references retrieved by each database used in the reviews. Unique references were included articles that had been found by only one database search. Those databases that contributed the most unique included references were then considered candidate databases to determine the most optimal combination of databases in the further analyses.

In Excel, we calculated the performance of each individual database and various combinations. Performance was measured using recall, precision, and number needed to read. See Table  1 for definitions of these measures. These values were calculated both for all reviews combined and per individual review.

Performance of a search can be expressed in different ways. Depending on the goal of the search, different measures may be optimized. In the case of a clinical question, precision is most important, as a practicing clinician does not have a lot of time to read through many articles in a clinical setting. When searching for a systematic review, recall is the most important aspect, as the researcher does not want to miss any relevant references. As our research is performed on systematic reviews, the main performance measure is recall.

We identified all included references that were uniquely identified by a single database. For the databases that retrieved the most unique included references, we calculated the number of references retrieved (after deduplication) and the number of included references that had been retrieved by all possible combinations of these databases, in total and per review. For all individual reviews, we determined the median recall, the minimum recall, and the percentage of reviews for which each single database or combination retrieved 100% recall.

For each review that we investigated, we determined what the recall was for all possible different database combinations of the most important databases. Based on these, we determined the percentage of reviews where that database combination had achieved 100% recall, more than 95%, more than 90%, and more than 80%. Based on the number of results per database both before and after deduplication as recorded at the time of searching, we calculated the ratio between the total number of results and the number of results for each database and combination.

Improvement of precision was calculated as the ratio between the original precision from the searches in all databases and the precision for each database and combination.

To compare our practice of database usage in systematic reviews against current practice as evidenced in the literature, we analyzed a set of 200 recent systematic reviews from PubMed. On 5 January 2017, we searched PubMed for articles with the phrase “systematic review” in the title. Starting with the most recent articles, we determined the databases searched either from the abstract or from the full text until we had data for 200 reviews. For the individual databases and combinations that were used in those reviews, we multiplied the frequency of occurrence in that set of 200 with the probability that the database or combination would lead to an acceptable recall (which we defined at 95%) that we had measured in our own data.

Our earlier research had resulted in 206 systematic reviews published between 2014 and July 2016, in which the first author was affiliated with Erasmus MC [ 21 ]. In 73 of these, the searches and results had been documented by the first author of this article at the time of the last search. Of those, 15 could not be included in this research, since they had not searched all databases we investigated here. Therefore, for this research, a total of 58 systematic reviews were analyzed. The references to these reviews can be found in Additional file 1 . An overview of the broad topical categories covered in these reviews is given in Table  2 . Many of the reviews were initiated by members of the departments of surgery and epidemiology. The reviews covered a wide variety of disease, none of which was present in more than 12% of the reviews. The interventions were mostly from the chemicals and drugs category, or surgical procedures. Over a third of the reviews were therapeutic, while slightly under a quarter answered an etiological question. Most reviews did not limit to certain study designs, 9% limited to RCTs only, and another 9% limited to other study types.

Together, these reviews included a total of 1830 references. Of these, 84 references (4.6%) had not been retrieved by our database searches and were not included in our analysis, leaving in total 1746 references. In our analyses, we combined the results from MEDLINE in Ovid and PubMed (the subset as supplied by publisher) into one database labeled MEDLINE.

Unique references per database

A total of 292 (17%) references were found by only one database. Table  3 displays the number of unique results retrieved for each single database. Embase retrieved the most unique included references, followed by MEDLINE, Web of Science, and Google Scholar. Cochrane CENTRAL is absent from the table, as for the five reviews limited to randomized trials, it did not add any unique included references. Subject-specific databases such as CINAHL, PsycINFO, and SportDiscus only retrieved additional included references when the topic of the review was directly related to their special content, respectively nursing, psychiatry, and sports medicine.

Overall performance

The four databases that had retrieved the most unique references (Embase, MEDLINE, Web of Science, and Google Scholar) were investigated individually and in all possible combinations (see Table  4 ). Of the individual databases, Embase had the highest overall recall (85.9%). Of the combinations of two databases, Embase and MEDLINE had the best results (92.8%). Embase and MEDLINE combined with either Google Scholar or Web of Science scored similarly well on overall recall (95.9%). However, the combination with Google Scholar had a higher precision and higher median recall, a higher minimum recall, and a higher proportion of reviews that retrieved all included references. Using both Web of Science and Google Scholar in addition to MEDLINE and Embase increased the overall recall to 98.3%. The higher recall from adding extra databases came at a cost in number needed to read (NNR). Searching only Embase produced an NNR of 57 on average, whereas, for the optimal combination of four databases, the NNR was 73.

Probability of appropriate recall

We calculated the recall for individual databases and databases in all possible combination for all reviews included in the research. Figure  1 shows the percentages of reviews where a certain database combination led to a certain recall. For example, in 48% of all systematic reviews, the combination of Embase and MEDLINE (with or without Cochrane CENTRAL; Cochrane CENTRAL did not add unique relevant references) reaches a recall of at least 95%. In 72% of studied systematic reviews, the combination of Embase, MEDLINE, Web of Science, and Google Scholar retrieved all included references. In the top bar, we present the results of the complete database searches relative to the total number of included references. This shows that many database searches missed relevant references.

Percentage of systematic reviews for which a certain database combination reached a certain recall. The X -axis represents the percentage of reviews for which a specific combination of databases, as shown on the y -axis, reached a certain recall (represented with bar colors). Abbreviations: EM Embase, ML MEDLINE, WoS Web of Science, GS Google Scholar. Asterisk indicates that the recall of all databases has been calculated over all included references. The recall of the database combinations was calculated over all included references retrieved by any database

Differences between domains of reviews

We analyzed whether the added value of Web of Science and Google Scholar was dependent of the domain of the review. For 55 reviews, we determined the domain. See Fig.  2 for the comparison of the recall of Embase, MEDLINE, and Cochrane CENTRAL per review for all identified domains. For all but one domain, the traditional combination of Embase, MEDLINE, and Cochrane CENTRAL did not retrieve enough included references. For four out of five systematic reviews that limited to randomized controlled trials (RCTs) only, the traditional combination retrieved 100% of all included references. However, for one review of this domain, the recall was 82%. Of the 11 references included in this review, one was found only in Google Scholar and one only in Web of Science.

Percentage of systematic reviews of a certain domain for which the combination Embase, MEDLINE and Cochrane CENTRAL reached a certain recall

Reduction in number of results

We calculated the ratio between the number of results found when searching all databases, including databases not included in our analyses, such as Scopus, PsycINFO, and CINAHL, and the number of results found searching a selection of databases. See Fig.  3 for the legend of the plots in Figs.  4 and 5 . Figure  4 shows the distribution of this value for individual reviews. The database combinations with the highest recall did not reduce the total number of results by large margins. Moreover, in combinations where the number of results was greatly reduced, the recall of included references was lower.

Legend of Figs. 3 and 4

The ratio between number of results per database combination and the total number of results for all databases

The ratio between precision per database combination and the total precision for all databases

Improvement of precision

To determine how searching multiple databases affected precision, we calculated for each combination the ratio between the original precision, observed when all databases were searched, and the precision calculated for different database combinations. Figure  5 shows the improvement of precision for 15 databases and database combinations. Because precision is defined as the number of relevant references divided by the number of total results, we see a strong correlation with the total number of results.

Status of current practice of database selection

From a set of 200 recent SRs identified via PubMed, we analyzed the databases that had been searched. Almost all reviews (97%) reported a search in MEDLINE. Other databases that we identified as essential for good recall were searched much less frequently; Embase was searched in 61% and Web of Science in 35%, and Google Scholar was only used in 10% of all reviews. For all individual databases or combinations of the four important databases from our research (MEDLINE, Embase, Web of Science, and Google Scholar), we multiplied the frequency of occurrence of that combination in the random set, with the probability we found in our research that this combination would lead to an acceptable recall of 95%. The calculation is shown in Table  5 . For example, around a third of the reviews (37%) relied on the combination of MEDLINE and Embase. Based on our findings, this combination achieves acceptable recall about half the time (47%). This implies that 17% of the reviews in the PubMed sample would have achieved an acceptable recall of 95%. The sum of all these values is the total probability of acceptable recall in the random sample. Based on these calculations, we estimate that the probability that this random set of reviews retrieved more than 95% of all possible included references was 40%. Using similar calculations, also shown in Table  5 , we estimated the probability that 100% of relevant references were retrieved is 23%.

Our study shows that, to reach maximum recall, searches in systematic reviews ought to include a combination of databases. To ensure adequate performance in searches (i.e., recall, precision, and number needed to read), we find that literature searches for a systematic review should, at minimum, be performed in the combination of the following four databases: Embase, MEDLINE (including Epub ahead of print), Web of Science Core Collection, and Google Scholar. Using that combination, 93% of the systematic reviews in our study obtained levels of recall that could be considered acceptable (> 95%). Unique results from specialized databases that closely match systematic review topics, such as PsycINFO for reviews in the fields of behavioral sciences and mental health or CINAHL for reviews on the topics of nursing or allied health, indicate that specialized databases should be used additionally when appropriate.

We find that Embase is critical for acceptable recall in a review and should always be searched for medically oriented systematic reviews. However, Embase is only accessible via a paid subscription, which generally makes it challenging for review teams not affiliated with academic medical centers to access. The highest scoring database combination without Embase is a combination of MEDLINE, Web of Science, and Google Scholar, but that reaches satisfactory recall for only 39% of all investigated systematic reviews, while still requiring a paid subscription to Web of Science. Of the five reviews that included only RCTs, four reached 100% recall if MEDLINE, Web of Science, and Google Scholar combined were complemented with Cochrane CENTRAL.

The Cochrane Handbook recommends searching MEDLINE, Cochrane CENTRAL, and Embase for systematic reviews of RCTs. For reviews in our study that included RCTs only, indeed, this recommendation was sufficient for four (80%) of the reviews. The one review where it was insufficient was about alternative medicine, specifically meditation and relaxation therapy, where one of the missed studies was published in the Indian Journal of Positive Psychology . The other study from the Journal of Advanced Nursing is indexed in MEDLINE and Embase but was only retrieved because of the addition of KeyWords Plus in Web of Science. We estimate more than 50% of reviews that include more study types than RCTs would miss more than 5% of included references if only traditional combination of MEDLINE, Embase, and Cochrane CENTAL is searched.

We are aware that the Cochrane Handbook [ 7 ] recommends more than only these databases, but further recommendations focus on regional and specialized databases. Though we occasionally used the regional databases LILACS and SciELO in our reviews, they did not provide unique references in our study. Subject-specific databases like PsycINFO only added unique references to a small percentage of systematic reviews when they had been used for the search. The third key database we identified in this research, Web of Science, is only mentioned as a citation index in the Cochrane Handbook, not as a bibliographic database. To our surprise, Cochrane CENTRAL did not identify any unique included studies that had not been retrieved by the other databases, not even for the five reviews focusing entirely on RCTs. If Erasmus MC authors had conducted more reviews that included only RCTs, Cochrane CENTRAL might have added more unique references.

MEDLINE did find unique references that had not been found in Embase, although our searches in Embase included all MEDLINE records. It is likely caused by difference in thesaurus terms that were added, but further analysis would be required to determine reasons for not finding the MEDLINE records in Embase. Although Embase covers MEDLINE, it apparently does not index every article from MEDLINE. Thirty-seven references were found in MEDLINE (Ovid) but were not available in Embase.com . These are mostly unique PubMed references, which are not assigned MeSH terms, and are often freely available via PubMed Central.

Google Scholar adds relevant articles not found in the other databases, possibly because it indexes the full text of all articles. It therefore finds articles in which the topic of research is not mentioned in title, abstract, or thesaurus terms, but where the concepts are only discussed in the full text. Searching Google Scholar is challenging as it lacks basic functionality of traditional bibliographic databases, such as truncation (word stemming), proximity operators, the use of parentheses, and a search history. Additionally, search strategies are limited to a maximum of 256 characters, which means that creating a thorough search strategy can be laborious.

Whether Embase and Web of Science can be replaced by Scopus remains uncertain. We have not yet gathered enough data to be able to make a full comparison between Embase and Scopus. In 23 reviews included in this research, Scopus was searched. In 12 reviews (52%), Scopus retrieved 100% of all included references retrieved by Embase or Web of Science. In the other 48%, the recall by Scopus was suboptimal, in one occasion as low as 38%.

Of all reviews in which we searched CINAHL and PsycINFO, respectively, for 6 and 9% of the reviews, unique references were found. For CINAHL and PsycINFO, in one case each, unique relevant references were found. In both these reviews, the topic was highly related to the topic of the database. Although we did not use these special topic databases in all of our reviews, given the low number of reviews where these databases added relevant references, and observing the special topics of those reviews, we suggest that these subject databases will only add value if the topic is related to the topic of the database.

Many articles written on this topic have calculated overall recall of several reviews, instead of the effects on all individual reviews. Researchers planning a systematic review generally perform one review, and they need to estimate the probability that they may miss relevant articles in their search. When looking at the overall recall, the combination of Embase and MEDLINE and either Google Scholar or Web of Science could be regarded sufficient with 96% recall. This number however is not an answer to the question of a researcher performing a systematic review, regarding which databases should be searched. A researcher wants to be able to estimate the chances that his or her current project will miss a relevant reference. However, when looking at individual reviews, the probability of missing more than 5% of included references found through database searching is 33% when Google Scholar is used together with Embase and MEDLINE and 30% for the Web of Science, Embase, and MEDLINE combination. What is considered acceptable recall for systematic review searches is open for debate and can differ between individuals and groups. Some reviewers might accept a potential loss of 5% of relevant references; others would want to pursue 100% recall, no matter what cost. Using the results in this research, review teams can decide, based on their idea of acceptable recall and the desired probability which databases to include in their searches.

Strengths and limitations

We did not investigate whether the loss of certain references had resulted in changes to the conclusion of the reviews. Of course, the loss of a minor non-randomized included study that follows the systematic review’s conclusions would not be as problematic as losing a major included randomized controlled trial with contradictory results. However, the wide range of scope, topic, and criteria between systematic reviews and their related review types make it very hard to answer this question.

We found that two databases previously not recommended as essential for systematic review searching, Web of Science and Google Scholar, were key to improving recall in the reviews we investigated. Because this is a novel finding, we cannot conclude whether it is due to our dataset or to a generalizable principle. It is likely that topical differences in systematic reviews may impact whether databases such as Web of Science and Google Scholar add value to the review. One explanation for our finding may be that if the research question is very specific, the topic of research might not always be mentioned in the title and/or abstract. In that case, Google Scholar might add value by searching the full text of articles. If the research question is more interdisciplinary, a broader science database such as Web of Science is likely to add value. The topics of the reviews studied here may simply have fallen into those categories, though the diversity of the included reviews may point to a more universal applicability.

Although we searched PubMed as supplied by publisher separately from MEDLINE in Ovid, we combined the included references of these databases into one measurement in our analysis. Until 2016, the most complete MEDLINE selection in Ovid still lacked the electronic publications that were already available in PubMed. These could be retrieved by searching PubMed with the subset as supplied by publisher. Since the introduction of the more complete MEDLINE collection Epub Ahead of Print , In-Process & Other Non-Indexed Citations , and Ovid MEDLINE® , the need to separately search PubMed as supplied by publisher has disappeared. According to our data, PubMed’s “as supplied by publisher” subset retrieved 12 unique included references, and it was the most important addition in terms of relevant references to the four major databases. It is therefore important to search MEDLINE including the “Epub Ahead of Print, In-Process, and Other Non-Indexed Citations” references.

These results may not be generalizable to other studies for other reasons. The skills and experience of the searcher are one of the most important aspects in the effectiveness of systematic review search strategies [ 23 , 24 , 25 ]. The searcher in the case of all 58 systematic reviews is an experienced biomedical information specialist. Though we suspect that searchers who are not information specialists or librarians would have a higher possibility of less well-constructed searches and searches with lower recall, even highly trained searchers differ in their approaches to searching. For this study, we searched to achieve as high a recall as possible, though our search strategies, like any other search strategy, still missed some relevant references because relevant terms had not been used in the search. We are not implying that a combined search of the four recommended databases will never result in relevant references being missed, rather that failure to search any one of these four databases will likely lead to relevant references being missed. Our experience in this study shows that additional efforts, such as hand searching, reference checking, and contacting key players, should be made to retrieve extra possible includes.

Based on our calculations made by looking at random systematic reviews in PubMed, we estimate that 60% of these reviews are likely to have missed more than 5% of relevant references only because of the combinations of databases that were used. That is with the generous assumption that the searches in those databases had been designed sensitively enough. Even when taking into account that many searchers consider the use of Scopus as a replacement of Embase, plus taking into account the large overlap of Scopus and Web of Science, this estimate remains similar. Also, while the Scopus and Web of Science assumptions we made might be true for coverage, they are likely very different when looking at recall, as Scopus does not allow the use of the full features of a thesaurus. We see that reviewers rarely use Web of Science and especially Google Scholar in their searches, though they retrieve a great deal of unique references in our reviews. Systematic review searchers should consider using these databases if they are available to them, and if their institution lacks availability, they should ask other institutes to cooperate on their systematic review searches.

The major strength of our paper is that it is the first large-scale study we know of to assess database performance for systematic reviews using prospectively collected data. Prior research on database importance for systematic reviews has looked primarily at whether included references could have theoretically been found in a certain database, but most have been unable to ascertain whether the researchers actually found the articles in those databases [ 10 , 12 , 16 , 17 , 26 ]. Whether a reference is available in a database is important, but whether the article can be found in a precise search with reasonable recall is not only impacted by the database’s coverage. Our experience has shown us that it is also impacted by the ability of the searcher, the accuracy of indexing of the database, and the complexity of terminology in a particular field. Because these studies based on retrospective analysis of database coverage do not account for the searchers’ abilities, the actual findings from the searches performed, and the indexing for particular articles, their conclusions lack immediate translatability into practice. This research goes beyond retrospectively assessed coverage to investigate real search performance in databases. Many of the articles reporting on previous research concluded that one database was able to retrieve most included references. Halladay et al. [ 10 ] and van Enst et al. [ 16 ] concluded that databases other than MEDLINE/PubMed did not change the outcomes of the review, while Rice et al. [ 17 ] found the added value of other databases only for newer, non-indexed references. In addition, Michaleff et al. [ 26 ] found that Cochrane CENTRAL included 95% of all RCTs included in the reviews investigated. Our conclusion that Web of Science and Google Scholar are needed for completeness has not been shared by previous research. Most of the previous studies did not include these two databases in their research.

We recommend that, regardless of their topic, searches for biomedical systematic reviews should combine Embase, MEDLINE (including electronic publications ahead of print), Web of Science (Core Collection), and Google Scholar (the 200 first relevant references) at minimum. Special topics databases such as CINAHL and PsycINFO should be added if the topic of the review directly touches the primary focus of a specialized subject database, like CINAHL for focus on nursing and allied health or PsycINFO for behavioral sciences and mental health. For reviews where RCTs are the desired study design, Cochrane CENTRAL may be similarly useful. Ignoring one or more of the databases that we identified as the four key databases will result in more precise searches with a lower number of results, but the researchers should decide whether that is worth the >increased probability of losing relevant references. This study also highlights once more that searching databases alone is, nevertheless, not enough to retrieve all relevant references.

Future research should continue to investigate recall of actual searches beyond coverage of databases and should consider focusing on the most optimal database combinations, not on single databases.

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Acknowledgements

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Melissa Rethlefsen receives funding in part from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR001067. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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WB, JK, and OF designed the study. WB designed the searches used in this study and gathered the data. WB and ML analyzed the data. WB drafted the first manuscript, which was revised critically by the other authors. All authors have approved the final manuscript.

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Reviews included in the research . References to the systematic reviews published by Erasmus MC authors that were included in the research. (DOCX 19 kb)

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Bramer, W.M., Rethlefsen, M.L., Kleijnen, J. et al. Optimal database combinations for literature searches in systematic reviews: a prospective exploratory study. Syst Rev 6 , 245 (2017). https://doi.org/10.1186/s13643-017-0644-y

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• Databases, bibliographic
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Systematic Reviews

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Doing the literature review: Selecting databases

• The literature review: why?
• Types of literature review

Selecting databases

• Scoping search
• Using a database thesaurus
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You have to decide which databases you will use in your literature search. To limit location bias, you have to use more than one database. Make an informed choice! In this module we list some database features you can take into account.

There are different types of literature databases:

• Discipline specific databases , such as PsycINFO, Philosopher's Index, Sociologial Abstracts and Business Source Premier, offer extra search tools, for example a thesaurus . The journals (some databases even select at the article level !) indexed in these databases are selected carefully, based on selection criteria. These databases may be available via different vendors/platforms.
• Multidisciplinary databases , such as Scopus and Web of Science, index academic journals from all disciplines, ranging from astronomy to zoology. These will help you find relevant articles in journals outside your own discipline, but non-relevant results are hard to avoid. Both Scopus and Web of Science are citation databases, which means that they track citations: you can see if an article is cited in other papers in the database and by which authors. JSTOR also offers access to journals from different disciplines, but be aware that this database has an archive function - for most journals you can’t access or search the most recently published volumes.
• Publishers databases , such as ScienceDirect (Elsevier), SpringerLink and Wiley Online Library, are limited to publications from a particular publisher. Therefore they are not suitable for a literature search for a review. But of course, you will use these databases to retrieve the full text of articles.

The Checklist Selecting Databases provides an overview of features you have to take into account when choosing the databases to use for your literature review.

What about Google Scholar?

Google Scholar indexes websites with scholarly articles – including websites of academic publishers, university repositories and personal websites of researchers . A major difference between Google Scholar and A&I databases is that Google Scholar doesn’t provide information about the indexed websites or journals. It’s hard to check if a particular journal is indexed cover-to-cover in Google Scholar. Google Scholar gives no definition of ‘scholarly’. Amongst the scholarly results you might get results from predatory publishers and papers written by students.

When you use Google Scholar for a search for your literature review, be aware that it can be hard to perform a structured, repeatable search:

• the advanced search options and filter options are limited
• you can't combine two search queries afterwards
• you can enter up to 256 characters in the search box of Google Scholar - if you want to include synonyms of search terms you often need more characters
• Google Scholar limits the results of any search query to 1000 papers, irrespectible of the number on top of the search results
• the information shown in the search results of Google Scholar can be very limited
• the metadata of articles (for example the publication year) can be incorrect, due to parsing errors
• specific journals or publishers might not be indexed by Google Scholar, due to technical reasons.

Google Scholar is a great tool for locating articles you know the titles of. In the Scholar settings you can add a Library link to the Erasmus University Library (see the module Get the most out of Google Scholar ) then follow the FULL-TEXT @ EUR links to the published version of an article in the EUR Library collection. 

TIP : Publish or Perish (also called PoP) is software used for citation analysis, based on Google Scholar citation data. The general citation search in Publish or Perish allows you to perform an Advanced Scholar Search query and analyse its results. The advantage is the presentation of the results: you can sort by author, year, times cited, publication and publisher. The abstract is not shown. It's possible to export the output, for example to Excel.

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Conducting Literature Reviews: Choosing Databases and Literature Sources

• Literature Review Basics
• Types of Literature Reviews
• Clarifying the Goal

Choosing Databases and Literature Sources

• Developing Search Strategies
• Choosing What Literature to Use
• Applying Screening Techniques
• Doing the Review
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Having clarified the goal of your literature review will help you determine where to look for your sources. Types of literature you may want to include are research databases (e.g., PsycARTICLES), Gray literature and databases containing it (e.g., PsycEXTRA), books and book chapters, and reference lists of pertinent topics. It is good to include at least two types of sources, such as PsycARTICLES and books. This would help to ensure that your literature review is complete. 

Factors Playing a Role in What You Choose

Factors playing a role in what you choose include: 

• Availability and Accessibility
• Relevance of the Literature
• Volume of Currently Existing Research
• Your Own Resources
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Some sources are freely available, while others are only available with a subscription. The library subscribes to many subscriptions but differences in accessibility and availability exist. If a topic is not very common, it could be more difficult to find available research on the subject.   

Not everything that you see in a search result will be relevant to your search. In some databases or catalogs, you can sort for relevance. Again, it may be more difficult to find relevant research if your topic is new and if little prior research exists on the subject.

Depending on what your topic is, the volume of currently existing research may vary. 

Your literature review would be dependent on the amount of time and other resources you have available (e.g., your library’s databases).   

Your literature review would be impacted about the goals and format of your project. For example, if you are conducting a literature review for a background section, less research would be needed than if you were writing a solo literature review. And if you are writing for an academic community more research would be needed than one for a general audience.

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Systematic Reviews: Step 3: Conduct Literature Searches

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Partner with a librarian, systematic searching process, choose a few databases, search with controlled vocabulary and keywords, acknowledge outdated or offensive terminology, helpful tip - building your search, use nesting, boolean operators, and field tags, build your search, translate to other databases and other searching methods, document the search, updating your review.

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In Step 3, you will design a search strategy to find all of the articles related to your research question. You will:

• Define the main concepts of your topic
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• List terms to describe each concept
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• Use field tags to tell the database where to search for terms
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There are many factors to think about when building a strong search strategy for systematic reviews. Librarians are available to provide support with this step of the process.

Click an item below to see how it applies to Step 3: Conduct Literature Searches.

Reporting your review with PRISMA

For PRISMA, there are specific items you will want to report from your search.  For this step, review the PRISMA-S checklist.

• PRISMA-S for Searching
• Specify all databases, registers, websites, organizations, reference lists, and other sources searched or consulted to identify studies. Specify the date when each source was last searched or consulted. Present the full search strategies for all databases, registers and websites, including any filters and limits used.
• For information on how to document database searches and other search methods on your PRISMA flow diagram, visit our FAQs "How do I document database searches on my PRISMA flow diagram?" and "How do I document a grey literature search for my PRISMA flow diagram?"

Managing your review with Covidence

For this step of the review, in Covidence you can:

• Document searches in Covidence review settings so all team members can view
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How a librarian can help with Step 3

When designing and conducting literature searches, a librarian can advise you on :

• How to create a search strategy with Boolean operators, database-specific syntax, subject headings, and appropriate keywords 
• How to apply previously published systematic review search strategies to your current search
• How to test your search strategy's performance 
• How to translate a search strategy from one database's preferred structure to another

The goal of a systematic retrieve is to find all results that are relevant to your topic. Because systematic review searches can be quite extensive and retrieve large numbers of results, an important aspect of systematic searching is limiting the number of irrelevant results that need to be screened. Librarians are experts trained in literature searching and systematic review methodology. Ask us a question or partner with a librarian to save time and improve the quality of your review. Our comparison chart detailing two tiers of partnership provides more information on how librarians can collaborate with and contribute to systematic review teams.

Search Process

• Use controlled vocabulary, if applicable
• Include synonyms/keyword terms
• Choose databases, websites, and/or registries to search
• Translate to other databases
• Search using other methods (e.g. hand searching)
• Validate and peer review the search

Databases can be multidisciplinary or subject specific. Choose the best databases for your research question. Databases index various journals, so in order to be comprehensive, it is important to search multiple databases when conducting a systematic review. Consider searching databases with more diverse or global coverage (i.e., Global Index Medicus) when appropriate. A list of frequently used databases is provided below. You can access UNC Libraries' full listing of databases on the HSL website (arranged alphabetically or by subject ).

Generally speaking, when literature searching, you are not searching the full-text article. Instead, you are searching certain citation data fields, like title, abstract, keyword, controlled vocabulary terms, and more. When developing a literature search, a good place to start is to identify searchable concepts of the research question, and then expand by adding other terms to describe those concepts. Read below for more information and examples on how to develop a literature search, as well as find tips and tricks for developing more comprehensive searches.

Identify search concepts and terms for each

Start by identifying the main concepts of your research question. If unsure, try using a question framework to help identify the main searchable concepts. PICO is one example of a question framework and is used specifically for clinical questions. If your research question doesn't fit into the PICO model well, view other examples of question frameworks and try another!

View our example in PICO format

Question: for patients 65 years and older, does an influenza vaccine reduce the future risk of pneumonia, controlled vocabulary.

Controlled vocabulary is a set of terminology assigned to citations to describe the content of each reference. Searching with controlled vocabulary can improve the relevancy of search results. Many databases assign controlled vocabulary terms to citations, but their naming schema is often specific to each database. For example, the controlled vocabulary system searchable via PubMed is MeSH, or Medical Subject Headings. More information on searching MeSH can be found on the HSL PubMed Ten Tips Legacy Guide .

Note: Controlled vocabulary may be outdated, and some databases allow users to submit requests to update terminology.

View Controlled Vocabulary for our example PICO

As mentioned above, databases with controlled vocabulary often use their own unique system. A listing of controlled vocabulary systems by database is shown below.

Keyword Terms

Not all citations are indexed with controlled vocabulary terms, however, so it is important to combine controlled vocabulary searches with keyword, or text word, searches. 

Authors often write about the same topic in varied ways and it is important to add these terms to your search in order to capture most of the literature. For example, consider these elements when developing a list of keyword terms for each concept:

• American versus British spelling
• hyphenated terms
• quality of life
• satisfaction
• vaccination
• influenza vaccination

There are several resources to consider when searching for synonyms. Scan the results of preliminary searches to identify additional terms. Look for synonyms, word variations, and other possibilities in Wikipedia, other encyclopedias or dictionaries, and databases. For example, PubChem lists additional drug names and chemical compounds.

Display Controlled Vocabulary and Keywords for our example PICO

Combining controlled vocabulary and text words in PubMed would look like this:

"Influenza Vaccines"[Mesh] OR "influenza vaccine" OR "influenza vaccines" OR "flu vaccine" OR "flu vaccines" OR "flu shot" OR "flu shots" OR "influenza virus vaccine" OR "influenza virus vaccines"

Social and cultural norms have been rapidly changing around the world. This has led to changes in the vocabulary used, such as when describing people or populations. Library and research terminology changes more slowly, and therefore can be considered outdated, unacceptable, or overly clinical for use in conversation or writing.

For our example with people 65 years and older, APA Style Guidelines recommend that researchers use terms like “older adults” and “older persons” and forgo terms like “senior citizens” and “elderly” that connote stereotypes. While these are current recommendations, researchers will recognize that terms like “elderly” have previously been used in the literature. Therefore, removing these terms from the search strategy may result in missed relevant articles. 

Research teams need to discuss current and outdated terminology and decide which terms to include in the search to be as comprehensive as possible. The research team or a librarian can search for currently preferred terms in glossaries, dictionaries, published guidelines, and governmental or organizational websites. The University of Michigan Library provides suggested wording to use in the methods section when antiquated, non-standard, exclusionary, or potentially offensive terms are included in the search.

Check the methods sections or supplementary materials of published systematic reviews for search strategies to see what terminology they used. This can help inform your search strategy by using MeSH terms or keywords you may not have thought of. However, be aware that search strategies will differ in their comprehensiveness.

You can also run a preliminary search for your topic, sort the results by Relevance or Best Match, and skim through titles and abstracts to identify terminology from relevant articles that you should include in your search strategy.

Nesting is a term that describes organizing search terms inside parentheses. This is important because, just like their function in math, commands inside a set of parentheses occur first. Parentheses let the database know in which order terms should be combined. 

Always combine terms for a single concept inside a parentheses set. For example: 

( "Influenza Vaccines"[Mesh] OR "influenza vaccine" OR "influenza vaccines" OR "flu vaccine" OR "flu vaccines" OR "flu shot" OR "flu shots" OR "influenza virus vaccine" OR "influenza virus vaccines" )

Additionally, you may nest a subset of terms for a concept inside a larger parentheses set, as seen below. Pay careful attention to the number of parenthesis sets and ensure they are matched, meaning for every open parentheses you also have a closed one.

( "Influenza Vaccines"[Mesh] OR "influenza vaccine" OR "influenza vaccines" OR "flu vaccine" OR "flu vaccines" OR "flu shot" OR "flu shots" OR "influenza virus vaccine" OR "influenza virus vaccines" OR   (( flu OR influenza ) AND ( vaccine OR vaccines OR vaccination OR immunization )))

Boolean operators

Boolean operators are used to combine terms in literature searches. Searches are typically organized using the Boolean operators OR or AND. OR is used to combine search terms for the same concept (i.e., influenza vaccine). AND is used to combine different concepts (i.e., influenza vaccine AND older adults AND pneumonia). An example of how Boolean operators can affect search retrieval is shown below. Using AND to combine the three concepts will only retrieve results where all are present. Using OR to combine the concepts will retrieve results that use all separately or together. It is important to note that, generally speaking, when you are performing a literature search you are only searching the title, abstract, keywords and other citation data. You are not searching the full-text of the articles.

The last major element to consider when building systematic literature searches are field tags. Field tags tell the database exactly where to search. For example, you can use a field tag to tell a database to search for a term in just the title, the title and abstract, and more. Just like with controlled vocabulary, field tag commands are different for every database.

If you do not manually apply field tags to your search, most databases will automatically search in a set of citation data points. Databases may also overwrite your search with algorithms if you do not apply field tags. For systematic review searching, best practice is to apply field tags to each term for reproducibility.

For example:

("Influenza Vaccines"[Mesh] OR "influenza vaccine"[tw] OR "influenza vaccines"[tw] OR "flu vaccine"[tw] OR "flu vaccines"[tw] OR "flu shot"[tw] OR "flu shots"[tw] OR "influenza virus vaccine"[tw] OR "influenza virus vaccines"[tw] OR ((flu[tw] OR influenza[tw]) AND (vaccine[tw] OR vaccines[tw] OR vaccination[tw] OR immunization[tw])))

View field tags for several health databases

For more information about how to use a variety of databases, check out our guides on searching.

• Searching PubMed guide Guide to searching Medline via the PubMed database
• Searching Embase guide Guide to searching Embase via embase.com
• Searching Scopus guide Guide to searching Scopus via scopus.com
• Searching EBSCO Databases guide Guide to searching CINAHL, PsycInfo, Global Health, & other databases via EBSCO

Combining search elements together

Organizational structure of literature searches is very important. Specifically, how terms are grouped (or nested) and combined with Boolean operators will drastically impact search results. These commands tell databases exactly how to combine terms together, and if done incorrectly or inefficiently, search results returned may be too broad or irrelevant.

For example, in PubMed:

(influenza OR flu) AND vaccine is a properly combined search and it produces around 50,000 results.

influenza OR flu AND vaccine is not properly combined.  Databases may read it as everything about influenza OR everything about (flu AND vaccine), which would produce more results than needed.

We recommend one or more of the following:

• put all your synonyms together inside a set of parentheses, then put AND between the closing parenthesis of one set and the opening parenthesis of the next set
• use a separate search box for each set of synonyms
• run each set of synonyms as a separate search, and then combine all your searches
• ask a librarian if your search produces too many or too few results

View the proper way to combine MeSH terms and Keywords for our example PICO

Question: for patients 65 years and older, does an influenza vaccine reduce the future risk of pneumonia , translating search strategies to other databases.

Databases often use their own set of terminology and syntax. When searching multiple databases, you need to adjust the search slightly to retrieve comparable results. Our sections on Controlled Vocabulary and Field Tags have information on how to build searches in different databases.  Resources to help with this process are listed below.

• Polyglot search A tool to translate a PubMed or Ovid search to other databases
• Search Translation Resources (Cornell) A listing of resources for search translation from Cornell University
• Advanced Searching Techniques (King's College London) A collection of advanced searching techniques from King's College London

Other searching methods

Hand searching.

Literature searches can be supplemented by hand searching. One of the most popular ways this is done with systematic reviews is by searching the reference list and citing articles of studies included in the review. Another method is manually browsing key journals in your field to make sure no relevant articles were missed. Other sources that may be considered for hand searching include: clinical trial registries, white papers and other reports, pharmaceutical or other corporate reports, conference proceedings, theses and dissertations, or professional association guidelines.

Searching grey literature

Grey literature typically refers to literature not published in a traditional manner and often not retrievable through large databases and other popular resources. Grey literature should be searched for inclusion in systematic reviews in order to reduce bias and increase thoroughness. There are several databases specific to grey literature that can be searched.

• Open Grey Grey literature for Europe
• OAIster A union catalog of millions of records representing open access resources from collections worldwide
• Grey Matters: a practical tool for searching health-related grey literature (CADTH) From CADTH, the Canadian Agency for Drugs and Technologies in Health, Grey Matters is a practical tool for searching health-related grey literature. The MS Word document covers a grey literature checklist, including national and international health technology assessment (HTA) web sites, drug and device regulatory agencies, clinical trial registries, health economics resources, Canadian health prevalence or incidence databases, and drug formulary web sites.
• Duke Medical Center Library: Searching for Grey Literature A good online compilation of resources by the Duke Medical Center Library.

Systematic review quality is highly dependent on the literature search(es) used to identify studies. To follow best practices for reporting search strategies, as well as increase reproducibility and transparency, document various elements of the literature search for your review. To make this process more clear, a statement and checklist for reporting literature searches has been developed and and can be found below.

• PRISMA-S: Reporting Literature Searches in Systematic Reviews
• Section 4.5 Cochrane Handbook - Documenting and reporting the search process

At a minimum, document and report certain elements, such as databases searched, including name (i.e., Scopus) and platform (i.e. Elsevier), websites, registries, and grey literature searched. In addition, this also may include citation searching and reaching out to experts in the field. Search strategies used in each database or source should be documented, along with any filters or limits, and dates searched. If a search has been updated or was built upon previous work, that should be noted as well. It is also helpful to document which search terms have been tested and decisions made for term inclusion or exclusion by the team. Last, any peer review process should be stated as well as the total number of records identified from each source and how deduplication was handled. 

If you have a librarian on your team who is creating and running the searches, they will handle the search documentation.

You can document search strategies in word processing software you are familiar with like Microsoft Word or Excel, or Google Docs or Sheets. A template, and separate example file, is provided below for convenience. 

• Search Strategy Documentation Template
• Search Strategy Documentation Example

*Some databases like PubMed are being continually updated with new technology and algorithms. This means that searches may retrieve different results than when originally run, even with the same filters, date limits, etc.

When you decide to update a systematic review search, there are two ways of identifying new articles:  

1. rerun the original search strategy without any changes. .

Rerun the original search strategy without making any changes.  Import the results into your citation manager, and remove all articles duplicated from the original set of search results.

2. Rerun the original search strategy and add an entry date filter.

Rerun the original search strategy and add a date filter for when the article was added to the database ( not the publication date).  An entry date filter will find any articles added to the results since you last ran the search, unlike a publication date filter, which would only find more recent articles.

Some examples of entry date filters for articles entered since December 31, 2021 are:

• PubMed:   AND ("2021/12/31"[EDAT] : "3000"[EDAT])
• Embase: AND [31-12-2021]/sd
• CINAHL:   AND EM 20211231-20231231
• PsycInfo: AND RD 20211231-20231231
• Scopus:   AND LOAD-DATE AFT 20211231  

Your PRISMA flow diagram

For more information about updating the PRISMA flow diagram for your systematic review, see the information on filling out a PRISMA flow diagram for review updates on the Step 8: Write the Review page of the guide.

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How to undertake a literature search: a step-by-step guide

Affiliation.

• 1 Literature Search Specialist, Library and Archive Service, Royal College of Nursing, London.
• PMID: 32279549
• DOI: 10.12968/bjon.2020.29.7.431

Undertaking a literature search can be a daunting prospect. Breaking the exercise down into smaller steps will make the process more manageable. This article suggests 10 steps that will help readers complete this task, from identifying key concepts to choosing databases for the search and saving the results and search strategy. It discusses each of the steps in a little more detail, with examples and suggestions on where to get help. This structured approach will help readers obtain a more focused set of results and, ultimately, save time and effort.

Keywords: Databases; Literature review; Literature search; Reference management software; Research questions; Search strategy.

• Databases, Bibliographic*
• Information Storage and Retrieval / methods*
• Nursing Research
• Review Literature as Topic*

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Literature Review: Conducting & Writing

• Steps for Conducting a Lit Review

1. Choose a topic. Define your research question.

2. decide on the scope of your review., 3. select the databases you will use to conduct your searches., 4. conduct your searches and find the literature. keep track of your searches, 5. review the literature..

• Finding "The Literature"
• Organizing/Writing
• APA Style This link opens in a new window
• Chicago: Notes Bibliography This link opens in a new window
• MLA Style This link opens in a new window
• Sample Literature Reviews

Disclaimer!!

Conducting a literature review is usually recursive, meaning that somewhere along the way, you'll find yourself repeating steps out-of-order.

That is actually a good sign.  

Reviewing the research should lead to more research questions and those questions will likely lead you to either revise your initial research question or go back and find more literature related to a more specific aspect of your research question.

Your literature review should be guided by a central research question.  Remember, it is not a collection of loosely related studies in a field but instead represents background and research developments related to a specific research question, interpreted and analyzed by you in a synthesized way.

• Make sure your research question is not too broad or too narrow.  Is it manageable?
• Begin writing down terms that are related to your question. These will be useful for searches later.
• If you have the opportunity, discuss your topic with your professor.

How many studies do you need to look at? How comprehensive should it be? How many years should it cover? 

Tip: This may depend on your assignment.  How many sources does the assignment require?

Make a list of the databases you will search.  Remember to include comprehensive databases such as WorldCat and Dissertations & Theses, if you need to.

Where to find databases:

• Find Databases by Subject UWF Databases categorized by discipline
• Find Databases via Research Guides Librarians create research guides for all of the disciplines on campus! Take advantage of their expertise and see what discipline-specific search strategies they recommend!
• Review the abstracts of research studies carefully. This will save you time.
• Write down the searches you conduct in each database so that you may duplicate them if you need to later (or avoid dead-end searches   that you'd forgotten you'd already tried).
• Use the bibliographies and references of research studies you find to locate others.
• Ask your professor or a scholar in the field if you are missing any key works in the field.
• Use RefWorks to keep track of your research citations. See the RefWorks Tutorial if you need help.

Some questions to help you analyze the research:

• What was the research question of the study you are reviewing? What were the authors trying to discover?
• Was the research funded by a source that could influence the findings?
• What were the research methodologies? Analyze its literature review, the samples and variables used, the results, and the conclusions. Does the research seem to be complete? Could it have been conducted more soundly? What further questions does it raise?
• If there are conflicting studies, why do you think that is?
• How are the authors viewed in the field? Has this study been cited?; if so, how has it been analyzed?

Tips: 

• Again, review the abstracts carefully.  
• Keep careful notes so that you may track your thought processes during the research process.
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• Last Updated: Mar 22, 2024 9:37 AM
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YSN Doctoral Programs: Steps in Conducting a Literature Review

• Biomedical Databases
• Global (Public Health) Databases
• Soc. Sci., History, and Law Databases
• Grey Literature
• Trials Registers
• Data and Statistics
• Public Policy
• Google Tips
• Recommended Books
• Steps in Conducting a Literature Review

What is a literature review?

A literature review is an integrated analysis -- not just a summary-- of scholarly writings and other relevant evidence related directly to your research question.  That is, it represents a synthesis of the evidence that provides background information on your topic and shows a association between the evidence and your research question.

A literature review may be a stand alone work or the introduction to a larger research paper, depending on the assignment.  Rely heavily on the guidelines your instructor has given you.

Why is it important?

A literature review is important because it:

• Explains the background of research on a topic.
• Demonstrates why a topic is significant to a subject area.
• Discovers relationships between research studies/ideas.
• Identifies major themes, concepts, and researchers on a topic.
• Identifies critical gaps and points of disagreement.
• Discusses further research questions that logically come out of the previous studies.

APA7 Style resources

APA Style Blog - for those harder to find answers

1. Choose a topic. Define your research question.

Your literature review should be guided by your central research question.  The literature represents background and research developments related to a specific research question, interpreted and analyzed by you in a synthesized way.

• Make sure your research question is not too broad or too narrow.  Is it manageable?
• Begin writing down terms that are related to your question. These will be useful for searches later.
• If you have the opportunity, discuss your topic with your professor and your class mates.

2. Decide on the scope of your review

How many studies do you need to look at? How comprehensive should it be? How many years should it cover? 

• This may depend on your assignment.  How many sources does the assignment require?

3. Select the databases you will use to conduct your searches.

Make a list of the databases you will search. 

Where to find databases:

• use the tabs on this guide
• Find other databases in the Nursing Information Resources web page
• More on the Medical Library web page
• ... and more on the Yale University Library web page

4. Conduct your searches to find the evidence. Keep track of your searches.

• Use the key words in your question, as well as synonyms for those words, as terms in your search. Use the database tutorials for help.
• Save the searches in the databases. This saves time when you want to redo, or modify, the searches. It is also helpful to use as a guide is the searches are not finding any useful results.
• Review the abstracts of research studies carefully. This will save you time.
• Use the bibliographies and references of research studies you find to locate others.
• Check with your professor, or a subject expert in the field, if you are missing any key works in the field.
• Ask your librarian for help at any time.
• Use a citation manager, such as EndNote as the repository for your citations. See the EndNote tutorials for help.

Review the literature

Some questions to help you analyze the research:

• What was the research question of the study you are reviewing? What were the authors trying to discover?
• Was the research funded by a source that could influence the findings?
• What were the research methodologies? Analyze its literature review, the samples and variables used, the results, and the conclusions.
• Does the research seem to be complete? Could it have been conducted more soundly? What further questions does it raise?
• If there are conflicting studies, why do you think that is?
• How are the authors viewed in the field? Has this study been cited? If so, how has it been analyzed?

Tips: 

• Review the abstracts carefully.  
• Keep careful notes so that you may track your thought processes during the research process.
• Create a matrix of the studies for easy analysis, and synthesis, across all of the studies.
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• Last Updated: Jan 4, 2024 10:52 AM
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Literature Reviews

Steps in the literature review process.

• What is a literature review?
• Define your research question
• Determine inclusion and exclusion criteria
• Choose databases and search
• Review Results
• Synthesize Results
• Analyze Results
• Librarian Support
• You may need to some exploratory searching of the literature to get a sense of scope, to determine whether you need to narrow or broaden your focus
• Identify databases that provide the most relevant sources, and identify relevant terms (controlled vocabularies) to add to your search strategy
• Finalize your research question
• Think about relevant dates, geographies (and languages), methods, and conflicting points of view
• Conduct searches in the published literature via the identified databases
• Check to see if this topic has been covered in other discipline's databases
• Examine the citations of on-point articles for keywords, authors, and previous research (via references) and cited reference searching.
• Save your search results in a citation management tool (such as Zotero, Mendeley or EndNote)
• De-duplicate your search results
• Make sure that you've found the seminal pieces -- they have been cited many times, and their work is considered foundational 
• Check with your professor or a librarian to make sure your search has been comprehensive
• Evaluate the strengths and weaknesses of individual sources and evaluate for bias, methodologies, and thoroughness
• Group your results in to an organizational structure that will support why your research needs to be done, or that provides the answer to your research question  
• Develop your conclusions
• Are there gaps in the literature?
• Where has significant research taken place, and who has done it?
• Is there consensus or debate on this topic?
• Which methodological approaches work best?
• For example: Background, Current Practices, Critics and Proponents, Where/How this study will fit in 
• Organize your citations and focus on your research question and pertinent studies
• Compile your bibliography

Note: The first four steps are the best points at which to contact a librarian. Your librarian can help you determine the best databases to use for your topic, assess scope, and formulate a search strategy.

Videos Tutorials about Literature Reviews

This 4.5 minute video from Academic Education Materials has a Creative Commons License and a British narrator.

Recommended Reading

• Last Updated: Oct 26, 2022 2:49 PM
• URL: https://guides.lib.utexas.edu/literaturereviews

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How to Conduct a Literature Review (Health Sciences and Beyond)

• What is a Literature Review?
• Developing a Research Question
• Selection Criteria

Choosing Search Terms

Filtering your search, translating between databases.

• Documenting Your Search
• Organize Key Findings
• Reference Management

Boolean Operators

Connecting terms with AND requires all terms to appear in the same article.

Connecting terms with OR results in articles that include one term, a combination of terms, or all of the terms.

Search Tip!

Keep in mind that search is not a linear process -- you will need to test and revise as you go. Terms that seem good may not find good results, or your topic may be too broad or too narrow.

If you would like assistance with your search, contact a librarian .

Before starting your database search, think about terms that can be used to describe the key concepts in your research question. Start your search with terms that you think make sense.  When you find citations that are highly relevant to your research, take a closer look at those records. Examine those records for two types of terms  that you can use in your search : subject headings  and keywords.

• Subject Heading : A single, assigned term that stands for a concept. For example, in PubMed, any paper that discusses acetylsalicylic  acid  would be assigned the Medical Subject Heading (MeSH) term aspirin . A search for the MeSH term  Aspirin  in PubMed should find papers written about aspirin  whether or not the word actually appears in the title or abstract.
• Keyword : Term used for a concept in everyday language. For example, if you need to find articles written about bedpans , the  Medical Subject Heading (MeSH)  term Toilet Facilities  in PubMed may be too broad. Just searching for bedpan OR bedpans  by typing this directly into the search box might work better.

Subject headings and keywords have different advantages and disadvantages. Keywords can retrieve new articles that do not yet have  subject headings assigned to them. You can also use keywords to capture alternative spellings. Subject headings, however, will help you find highly relevant articles, and may mitigate the need to search for synonyms.

When you conduct your search, consider whether it makes sense to use keywords, subject headings, or both.

See "Documenting Your Search"  to learn how to keep track of useful terms.

Many databases allow you to filter your search. You can usually find filters are on the left-hand side of your results page . Based on your selection criteria , you may want to filter your results based on:

• Publication date range (e.g. last 10 years)
• Source (e.g. journal name)
• Article type (e.g. review, research report, etc.)
• Study type (e.g. randomized-control trial, cohort, etc.)

If you're searching different databases for information, keep in mind that you may need to adjust your search terms for each database. For instance, the equivalent subject heading for  " Heart, Artificial" in  PubMed is "Heart, Mechanical" in CINAHL. Additionally, because CINAHL is an allied health and nursing database, you will find specialized subject headings such as "Toileting" in CINAHL that you won't find in PubMed.

Keywords are more likely to stay consistent across databases.

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• Next: Documenting Your Search >>
• Last Updated: Mar 15, 2024 12:22 PM
• URL: https://guides.library.vcu.edu/health-sciences-lit-review

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Literature Search: Databases and Gray Literature

The literature search.

• A systematic review search includes a search of databases, gray literature, personal communications, and a handsearch of high impact journals in the related field.  See our list of recommended databases and gray literature sources on this page.
• a comprehensive literature search can not be dependent on a single database, nor on bibliographic databases only.
• inclusion of multiple databases helps avoid publication bias (georaphic bias or bias against publication of negative results).
• The Cochrane Collaboration recommends PubMed, Embase and the Cochrane Central Register of Controlled Trials (CENTRAL) at a minimum.     
• NOTE:  The Cochrane Collaboration and the IOM recommend that the literature search be conducted by librarians or persons with extensive literature search experience. Please contact the NIH Librarians for assistance with the literature search component of your systematic review. 

Cochrane Library

A collection of six databases that contain different types of high-quality, independent evidence to inform healthcare decision-making. Search the Cochrane Central Register of Controlled Trials here.

European database of biomedical and pharmacologic literature.

PubMed comprises more than 21 million citations for biomedical literature from MEDLINE, life science journals, and online books.

Largest abstract and citation database of peer-reviewed literature and quality web sources. Contains conference papers.

Web of Science

World's leading citation databases. Covers over 12,000 of the highest impact journals worldwide, including Open Access journals and over 150,000 conference proceedings. Coverage in the sciences, social sciences, arts, and humanities, with coverage to 1900.

Subject Specific Databases

APA PsycINFO

Over 4.5 million abstracts of peer-reviewed literature in the behavioral and social sciences. Includes conference papers, book chapters, psychological tests, scales and measurement tools.

CINAHL Plus

Comprehensive journal index to nursing and allied health literature, includes books, nursing dissertations, conference proceedings, practice standards and book chapters.

Latin American and Caribbean health sciences literature database

Gray Literature

• Gray Literature  is the term for information that falls outside the mainstream of published journal and mongraph literature, not controlled by commercial publishers
• hard to find studies, reports, or dissertations
• conference abstracts or papers
• governmental or private sector research
• clinical trials - ongoing or unpublished
• experts and researchers in the field     
• Library catalogs
• Professional association websites
• Google Scholar  - Search scholarly literature across many disciplines and sources, including theses, books, abstracts and articles.
• Dissertation Abstracts - dissertation and theses database - NIH Library biomedical librarians can access and search for you.
• NTIS  - central resource for government-funded scientific, technical, engineering, and business related information.
• AHRQ  - agency for healthcare research and quality
• Open Grey  - system for information on grey literature in Europe. Open access to 700,000 references to the grey literature.
• World Health Organization  - providing leadership on global health matters, shaping the health research agenda, setting norms and standards, articulating evidence-based policy options, providing technical support to countries and monitoring and assessing health trends.
• New York Academy of Medicine Grey Literature Report  - a bimonthly publication of The New York Academy of Medicine (NYAM) alerting readers to new gray literature publications in health services research and selected public health topics. NOTE: Discontinued as of Jan 2017, but resources are still accessible.
• Gray Source Index
• OpenDOAR - directory of academic repositories
• International Clinical Trials Registery Platform  - from the World Health Organization
• Australian New Zealand Clinical Trials Registry
• Brazilian Clinical Trials Registry
• Chinese Clinical Trial Registry - 
• ClinicalTrials.gov   - U.S.  and international federally and privately supported clinical trials registry and results database
• Clinical Trials Registry  - India
• EU clinical Trials Register
• Japan Primary Registries Network  
• Pan African Clinical Trials Registry

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A systematic approach to searching: an efficient and complete method to develop literature searches

Associated data.

Creating search strategies for systematic reviews, finding the best balance between sensitivity and specificity, and translating search strategies between databases is challenging. Several methods describe standards for systematic search strategies, but a consistent approach for creating an exhaustive search strategy has not yet been fully described in enough detail to be fully replicable. The authors have established a method that describes step by step the process of developing a systematic search strategy as needed in the systematic review. This method describes how single-line search strategies can be prepared in a text document by typing search syntax (such as field codes, parentheses, and Boolean operators) before copying and pasting search terms (keywords and free-text synonyms) that are found in the thesaurus. To help ensure term completeness, we developed a novel optimization technique that is mainly based on comparing the results retrieved by thesaurus terms with those retrieved by the free-text search words to identify potentially relevant candidate search terms. Macros in Microsoft Word have been developed to convert syntaxes between databases and interfaces almost automatically. This method helps information specialists in developing librarian-mediated searches for systematic reviews as well as medical and health care practitioners who are searching for evidence to answer clinical questions. The described method can be used to create complex and comprehensive search strategies for different databases and interfaces, such as those that are needed when searching for relevant references for systematic reviews, and will assist both information specialists and practitioners when they are searching the biomedical literature.

INTRODUCTION

Librarians and information specialists are often involved in the process of preparing and completing systematic reviews (SRs), where one of their main tasks is to identify relevant references to include in the review [ 1 ]. Although several recommendations for the process of searching have been published [ 2 – 6 ], none describe the development of a systematic search strategy from start to finish.

Traditional methods of SR search strategy development and execution are highly time consuming, reportedly requiring up to 100 hours or more [ 7 , 8 ]. The authors wanted to develop systematic and exhaustive search strategies more efficiently, while preserving the high sensitivity that SR search strategies necessitate. In this article, we describe the method developed at Erasmus University Medical Center (MC) and demonstrate its use through an example search. The efficiency of the search method and outcome of 73 searches that have resulted in published reviews are described in a separate article [ 9 ].

As we aimed to describe the creation of systematic searches in full detail, the method starts at a basic level with the analysis of the research question and the creation of search terms. Readers who are new to SR searching are advised to follow all steps described. More experienced searchers can consider the basic steps to be existing knowledge that will already be part of their normal workflow, although step 4 probably differs from general practice. Experienced searchers will gain the most from reading about the novelties in the method as described in steps 10–13 and comparing the examples given in the supplementary appendix to their own practice.

CREATING A SYSTEMATIC SEARCH STRATEGY

Our methodology for planning and creating a multi-database search strategy consists of the following steps:

• Determine a clear and focused question
• Describe the articles that can answer the question
• Decide which key concepts address the different elements of the question
• Decide which elements should be used for the best results
• Choose an appropriate database and interface to start with
• Document the search process in a text document
• Identify appropriate index terms in the thesaurus of the first database
• Identify synonyms in the thesaurus
• Add variations in search terms
• Use database-appropriate syntax, with parentheses, Boolean operators, and field codes
• Optimize the search
• Evaluate the initial results
• Check for errors
• Translate to other databases
• Test and reiterate

Each step in the process is reflected by an example search described in the supplementary appendix .

1. Determine a clear and focused question

A systematic search can best be applied to a well-defined and precise research or clinical question. Questions that are too broad or too vague cannot be answered easily in a systematic way and will generally result in an overwhelming number of search results. On the other hand, a question that is too specific will result into too few or even zero search results. Various papers describe this process in more detail [ 10 – 12 ].

2. Describe the articles that can answer the question

Although not all clinical or research questions can be answered in the literature, the next step is to presume that the answer can indeed be found in published studies. A good starting point for a search is hypothesizing what the research that can answer the question would look like. These hypothetical (when possible, combined with known) articles can be used as guidance for constructing the search strategy.

3. Decide which key concepts address the different elements of the question

Key concepts are the topics or components that the desired articles should address, such as diseases or conditions, actions, substances, settings, domains (e.g., therapy, diagnosis, etiology), or study types. Key concepts from the research question can be grouped to create elements in the search strategy.

Elements in a search strategy do not necessarily follow the patient, intervention, comparison, outcome (PICO) structure or any other related structure. Using the PICO or another similar framework as guidance can be helpful to consider, especially in the inclusion and exclusion review stage of the SR, but this is not necessary for good search strategy development [ 13 – 15 ]. Sometimes concepts from different parts of the PICO structure can be grouped together into one search element, such as when the desired outcome is frequently described in a certain study type.

4. Decide which elements should be used for the best results

Not all elements of a research question should necessarily be used in the search strategy. Some elements are less important than others or may unnecessarily complicate or restrict a search strategy. Adding an element to a search strategy increases the chance of missing relevant references. Therefore, the number of elements in a search strategy should remain as low as possible to optimize recall.

Using the schema in Figure 1 , elements can be ordered by their specificity and importance to determine the best search approach. Whether an element is more specific or more general can be measured objectively by the number of hits retrieved in a database when searching for a key term representing that element. Depending on the research question, certain elements are more important than others. If articles (hypothetically or known) exist that can answer the question but lack a certain element in their titles, abstracts, or keywords, that element is unimportant to the question. An element can also be unimportant because of expected bias or an overlap with another element.

Schema for determining the optimal order of elements

Bias in elements

The choice of elements in a search strategy can introduce bias through use of overly specific terminology or terms often associated with positive outcomes. For the question “does prolonged breastfeeding improve intelligence outcomes in children?,” searching specifically for the element of duration will introduce bias, as articles that find a positive effect of prolonged breastfeeding will be much more likely to mention time factors in their titles or abstracts.

Overlapping elements

Elements in a question sometimes overlap in their meaning. Sometimes certain therapies are interventions for one specific disease. The Lichtenstein technique, for example, is a repair method for inguinal hernias. There is no need to include an element of “inguinal hernias” to a search for the effectiveness of the Lichtenstein therapy. Likewise, sometimes certain diseases are only found in certain populations. Adding such an overlapping element could lead to missing relevant references.

The elements to use in a search strategy can be found in the plot of elements in Figure 1 , by following the top row from left to right. For this method, we recommend starting with the most important and specific elements. Then, continue with more general and important elements until the number of results is acceptable for screening. Determining how many results are acceptable for screening is often a matter of negotiation with the SR team.

5. Choose an appropriate database and interface to start with

Important factors for choosing databases to use are the coverage and the presence of a thesaurus. For medically oriented searches, the coverage and recall of Embase, which includes the MEDLINE database, are superior to those of MEDLINE [ 16 ]. Each of these two databases has its own thesaurus with its own unique definitions and structure. Because of the complexity of the Embase thesaurus, Emtree, which contains much more specific thesaurus terms than the MEDLINE Medical Subject Headings (MeSH) thesaurus, translation from Emtree to MeSH is easier than the other way around. Therefore, we recommend starting in Embase.

MEDLINE and Embase are available through many different vendors and interfaces. The choice of an interface and primary database is often determined by the searcher’s accessibility. For our method, an interface that allows searching with proximity operators is desirable, and full functionality of the thesaurus, including explosion of narrower terms, is crucial. We recommend developing a personal workflow that always starts with one specific database and interface.

6. Document the search process in a text document

We advise designing and creating the complete search strategies in a log document, instead of directly in the database itself, to register the steps taken and to make searches accountable and reproducible. The developed search strategies can be copied and pasted into the desired databases from the log document. This way, the searcher is in control of the whole process. Any change to the search strategy should be done in the log document, assuring that the search strategy in the log is always the most recent.

7. Identify appropriate index terms in the thesaurus of the first database

Searches should start by identifying appropriate thesaurus terms for the desired elements. The thesaurus of the database is searched for matching index terms for each key concept. We advise restricting the initial terms to the most important and most relevant terms. Later in the process, more general terms can be added in the optimization process, in which the effect on the number of hits, and thus the desirability of adding these terms, can be evaluated more easily.

Several factors can complicate the identification of thesaurus terms. Sometimes, one thesaurus term is found that exactly describes a specific element. In contrast, especially in more general elements, multiple thesaurus terms can be found to describe one element. If no relevant thesaurus terms have been found for an element, free-text terms can be used, and possible thesaurus terms found in the resulting references can be added later (step 11).

Sometimes, no distinct thesaurus term is available for a specific key concept that describes the concept in enough detail. In Emtree, one thesaurus term often combines two or more elements. The easiest solution for combining these terms for a sensitive search is to use such a thesaurus term in all elements where it is relevant. Examples are given in the supplementary appendix .

8. Identify synonyms in the thesaurus

Most thesauri offer a list of synonyms on their term details page (named Synonyms in Emtree and Entry Terms in MeSH). To create a sensitive search strategy for SRs, these terms need to be searched as free-text keywords in the title and abstract fields, in addition to searching their associated thesaurus terms.

The Emtree thesaurus contains more synonyms (300,000) than MeSH does (220,000) [ 17 ]. The difference in number of terms is even higher considering that many synonyms in MeSH are permuted terms (i.e., inversions of phrases using commas).

Thesaurus terms are ordered in a tree structure. When searching for a more general thesaurus term, the more specific (narrower) terms in the branches below that term will also be searched (this is frequently referred to as “exploding” a thesaurus term). However, to perform a sensitive search, all relevant variations of the narrower terms must be searched as free-text keywords in the title or abstract, in addition to relying on the exploded thesaurus term. Thus, all articles that describe a certain narrower topic in their titles and abstracts will already be retrieved before MeSH terms are added.

9. Add variations in search terms (e.g., truncation, spelling differences, abbreviations, opposites)

Truncation allows a searcher to search for words beginning with the same word stem. A search for therap* will, thus, retrieve therapy, therapies, therapeutic, and all other words starting with “therap.” Do not truncate a word stem that is too short. Also, limitations of interfaces should be taken into account, especially in PubMed, where the number of search term variations that can be found by truncation is limited to 600.

Databases contain references to articles using both standard British and American English spellings. Both need to be searched as free-text terms in the title and abstract. Alternatively, many interfaces offer a certain code to replace zero or one characters, allowing a search for “pediatric” or “paediatric” as “p?ediatric.” Table 1 provides a detailed description of the syntax for different interfaces.

Field codes in five most used interfaces for biomedical literature searching

Searching for abbreviations can identify extra, relevant references and retrieve more irrelevant ones. The search can be more focused by combining the abbreviation with an important word that is relevant to its meaning or by using the Boolean “NOT” to exclude frequently observed, clearly irrelevant results. We advise that searchers do not exclude all possible irrelevant meanings, as it is very time consuming to identify all the variations, it will result in unnecessarily complicated search strategies, and it may lead to erroneously narrowing the search and, thereby, reduce recall.

Searching partial abbreviations can be useful for retrieving relevant references. For example, it is very likely that an article would mention osteoarthritis (OA) early in the abstract, replacing all further occurrences of osteoarthritis with OA . Therefore, it may not contain the phrase “hip osteoarthritis” but only “hip oa.”

It is also important to search for the opposites of search terms to avoid bias. When searching for “disease recurrence,” articles about “disease free” may be relevant as well. When the desired outcome is survival , articles about mortality may be relevant.

10. Use database-appropriate syntax, with parentheses, Boolean operators, and field codes

Different interfaces require different syntaxes, the special set of rules and symbols unique to each database that define how a correctly constructed search operates. Common syntax components include the use of parentheses and Boolean operators such as “AND,” “OR,” and “NOT,” which are available in all major interfaces. An overview of different syntaxes for four major interfaces for bibliographic medical databases (PubMed, Ovid, EBSCOhost, Embase.com, and ProQuest) is shown in Table 1 .

Creating the appropriate syntax for each database, in combination with the selected terms as described in steps 7–9, can be challenging. Following the method outlined below simplifies the process:

• Create single-line queries in a text document (not combining multiple record sets), which allows immediate checking of the relevance of retrieved references and efficient optimization.
• Type the syntax (Boolean operators, parentheses, and field codes) before adding terms, which reduces the chance that errors are made in the syntax, especially in the number of parentheses.
• Use predefined proximity structures including parentheses, such as (() ADJ3 ()) in Ovid, that can be reused in the query when necessary.
• Use thesaurus terms separately from free-text terms of each element. Start an element with all thesaurus terms (using “OR”) and follow with the free-text terms. This allows the unique optimization methods as described in step 11.
• When adding terms to an existing search strategy, pay close attention to the position of the cursor. Make sure to place it appropriately either in the thesaurus terms section, in the title/abstract section, or as an addition (broadening) to an existing proximity search.

The supplementary appendix explains the method of building a query in more detail, step by step for different interfaces: PubMed, Ovid, EBSCOhost, Embase.com, and ProQuest. This method results in a basic search strategy designed to retrieve some relevant references upon which a more thorough search strategy can be built with optimization such as described in step 11.

11. Optimize the search

The most important question when performing a systematic search is whether all (or most) potentially relevant articles have been retrieved by the search strategy. This is also the most difficult question to answer, since it is unknown which and how many articles are relevant. It is, therefore, wise first to broaden the initial search strategy, making the search more sensitive, and then check if new relevant articles are found by comparing the set results (i.e., search for Strategy #2 NOT Strategy #1 to see the unique results).

A search strategy should be tested for completeness. Therefore, it is necessary to identify extra, possibly relevant search terms and add them to the test search in an OR relationship with the already used search terms. A good place to start, and a well-known strategy, is scanning the top retrieved articles when sorted by relevance, looking for additional relevant synonyms that could be added to the search strategy.

We have developed a unique optimization method that has not been described before in the literature. This method often adds valuable extra terms to our search strategy and, therefore, extra, relevant references to our search results. Extra synonyms can be found in articles that have been assigned a certain set of thesaurus terms but that lack synonyms in the title and/or abstract that are already present in the current search strategy. Searching for thesaurus terms NOT free-text terms will help identify missed free-text terms in the title or abstract. Searching for free-text terms NOT thesaurus terms will help identify missed thesaurus terms. If this is done repeatedly for each element, leaving the rest of the query unchanged, this method will help add numerous relevant terms to the query. These steps are explained in detail for five different search platforms in the supplementary appendix .

12. Evaluate the initial results

The results should now contain relevant references. If the interface allows relevance ranking, use that in the evaluation. If you know some relevant references that should be included in the research, search for those references specifically; for example, combine a specific (first) author name with a page number and the publication year. Check whether those references are retrieved by the search. If the known relevant references are not retrieved by the search, adapt the search so that they are. If it is unclear which element should be adapted to retrieve a certain article, combine that article with each element separately.

Different outcomes are desired for different types of research questions. For instance, in the case of clinical question answering, the researcher will not be satisfied with many references that contain a lot of irrelevant references. A clinical search should be rather specific and is allowed to miss a relevant reference. In the case of an SR, the researchers do not want to miss any relevant reference and are willing to handle many irrelevant references to do so. The search for references to include in an SR should be very sensitive: no included reference should be missed. A search that is too specific or too sensitive for the intended goal can be adapted to become more sensitive or specific. Steps to increase sensitivity or specificity of a search strategy can be found in the supplementary appendix .

13. Check for errors

Errors might not be easily detected. Sometimes clues can be found in the number of results, either when the number of results is much higher or lower than expected or when many retrieved references are not relevant. However, the number expected is often unknown, and very sensitive search strategies will always retrieve many irrelevant articles. Each query should, therefore, be checked for errors.

One of the most frequently occurring errors is missing the Boolean operator “OR.” When no “OR” is added between two search terms, many interfaces automatically add an “AND,” which unintentionally reduces the number of results and likely misses relevant references. One good strategy to identify missing “OR”s is to go to the web page containing the full search strategy, as translated by the database, and using Ctrl-F search for “AND.” Check whether the occurrences of the “AND” operator are deliberate.

Ideally, search strategies should be checked by other information specialists [ 18 ]. The Peer Review of Electronic Search Strategies (PRESS) checklist offers good guidance for this process [ 4 ]. Apart from the syntax (especially Boolean operators and field codes) of the search strategy, it is wise to have the search terms checked by the clinician or researcher familiar with the topic. At Erasmus MC, researchers and clinicians are involved during the complete process of structuring and optimizing the search strategy. Each word is added after the combined decision of the searcher and the researcher, with the possibility of directly comparing results with and without the new term.

14. Translate to other databases

To retrieve as many relevant references as possible, one has to search multiple databases. Translation of complex and exhaustive queries between different databases can be very time consuming and cumbersome. The single-line search strategy approach detailed above allows quick translations using the find and replace method in Microsoft Word (<Ctrl-H>).

At Erasmus MC, macros based on the find-and-replace method in Microsoft Word have been developed for easy and fast translation between the most used databases for biomedical and health sciences questions. The schema that is followed for the translation between databases is shown in Figure 2 . Most databases simply follow the structure set by the Embase.com search strategy. The translation from Emtree terms to MeSH terms for MEDLINE in Ovid often identifies new terms that need to be added to the Embase.com search strategy before the translation to other databases.

Schematic representation of translation between databases used at Erasmus University Medical Center

Dotted lines represent databases that are used in less than 80% of the searches.

Using five different macros, a thoroughly optimized query in Embase.com can be relatively quickly translated into eight major databases. Basic search strategies will be created to use in many, mostly smaller, databases, because such niche databases often do not have extensive thesauri or advanced syntax options. Also, there is not much need to use extensive syntax because the number of hits and, therefore, the amount of noise in these databases is generally low. In MEDLINE (Ovid), PsycINFO (Ovid), and CINAHL (EBSCOhost), the thesaurus terms must be adapted manually, as each database has its own custom thesaurus. These macros and instructions for their installation, use, and adaptation are available at bit.ly/databasemacros.

15. Test and reiterate

Ideally, exhaustive search strategies should retrieve all references that are covered in a specific database. For SR search strategies, checking searches for their recall is advised. This can be done after included references have been determined by the authors of the systematic review. If additional papers have been identified through other non-database methods (i.e., checking references in included studies), results that were not identified by the database searches should be examined. If these results were available in the databases but not located by the search strategy, the search strategy should be adapted to try to retrieve these results, as they may contain terms that were omitted in the original search strategies. This may enable the identification of additional relevant results.

A methodology for creating exhaustive search strategies has been created that describes all steps of the search process, starting with a question and resulting in thorough search strategies in multiple databases. Many of the steps described are not new, but together, they form a strong method creating high-quality, robust searches in a relatively short time frame.

Our methodology is intended to create thoroughness for literature searches. The optimization method, as described in step 11, will identify missed synonyms or thesaurus terms, unlike any other method that largely depends on predetermined keywords and synonyms. Using this method results in a much quicker search process, compared to traditional methods, especially because of the easier translation between databases and interfaces (step 13). The method is not a guarantee for speed, since speed depends on many factors, including experience. However, by following the steps and using the tools as described above, searchers can gain confidence first and increase speed through practice.

What is new?

This method encourages searchers to start their search development process using empty syntax first and later adding the thesaurus terms and free-text synonyms. We feel this helps the searcher to focus on the search terms, instead of on the structure of the search query. The optimization method in which new terms are found in the already retrieved articles is used in some other institutes as well but has to our knowledge not been described in the literature. The macros to translate search strategies between interfaces are unique in this method.

What is different compared to common practice?

Traditionally, librarians and information specialists have focused on creating complex, multi-line (also called line-by-line) search strategies, consisting of multiple record sets, and this method is frequently advised in the literature and handbooks [ 2 , 19 – 21 ]. Our method, instead, uses single-line searches, which is critical to its success. Single-line search strategies can be easily adapted by adding or dropping a term without having to recode numbers of record sets, which would be necessary in multi-line searches. They can easily be saved in a text document and repeated by copying and pasting for search updates. Single-line search strategies also allow easy translation to other syntaxes using find-and-replace technology to update field codes and other syntax elements or using macros (step 13).

When constructing a search strategy, the searcher might experience that certain parentheses in the syntax are unnecessary, such as parentheses around all search terms in the title/abstract portion, if there is only one such term, there are double parentheses in the proximity statement, or one of the word groups exists for only one word. One might be tempted to omit those parentheses for ease of reading and management. However, during the optimization process, the searcher is likely to find extra synonyms that might consist of one word. To add those terms to the first query (with reduced parentheses) requires adding extra parentheses (meticulously placing and counting them), whereas, in the latter search, it only requires proper placement of those terms.

Many search methods highly depend on the PICO framework. Research states that often PICO or PICOS is not suitable for every question [ 22 , 23 ]. There are other acronyms than PICO—such as sample, phenomenon of interest, design, evaluation, research type (SPIDER) [ 24 ]—but each is just a variant. In our method, the most important and specific elements of a question are being analyzed for building the best search strategy.

Though it is generally recommended that searchers search both MEDLINE and Embase, most use MEDLINE as the starting point. It is considered the gold standard for biomedical searching, partially due to historical reasons, since it was the first of its kind, and more so now that it is freely available via the PubMed interface. Our method can be used with any database as a starting point, but we use Embase instead of MEDLINE or another database for a number of reasons. First, Embase provides both unique content and the complete content of MEDLINE. Therefore, searching Embase will be, by definition, more complete than searching MEDLINE only. Second, the number of terms in Emtree (the Embase thesaurus) is three times as high as that of MeSH (the MEDLINE thesaurus). It is easier to find MeSH terms after all relevant Emtree terms have been identified than to start with MeSH and translate to Emtree.

At Erasmus MC, the researchers sit next to the information specialist during most of the search strategy design process. This way, the researchers can deliver immediate feedback on the relevance of proposed search terms and retrieved references. The search team then combines knowledge about databases with knowledge about the research topic, which is an important condition to create the highest quality searches.

Limitations of the method

One disadvantage of single-line searches compared to multi-line search strategies is that errors are harder to recognize. However, with the methods for optimization as described (step 11), errors are recognized easily because missed synonyms and spelling errors will be identified during the process. Also problematic is that more parentheses are needed, making it more difficult for the searcher and others to assess the logic of the search strategy. However, as parentheses and field codes are typed before the search terms are added (step 10), errors in parentheses can be prevented.

Our methodology works best if used in an interface that allows proximity searching. It is recommended that searchers with access to an interface with proximity searching capabilities select one of those as the initial database to develop and optimize the search strategy. Because the PubMed interface does not allow proximity searches, phrases or Boolean “AND” combinations are required. Phrase searching complicates the process and is more specific, with the higher risk of missing relevant articles, and using Boolean “AND” combinations increases sensitivity but at an often high loss of specificity. Due to some searchers’ lack of access to expensive databases or interfaces, the freely available PubMed interface may be necessary to use, though it should never be the sole database used for an SR [ 2 , 16 , 25 ]. A limitation of our method is that it works best with subscription-based and licensed resources.

Another limitation is the customization of the macros to a specific institution’s resources. The macros for the translation between different database interfaces only work between the interfaces as described. To mitigate this, we recommend using the find-and-replace functionality of text editors like Microsoft Word to ease the translation of syntaxes between other databases. Depending on one’s institutional resources, custom macros can be developed using similar methods.

Results of the method

Whether this method results in exhaustive searches where no important article is missed is difficult to determine, because the number of relevant articles is unknown for any topic. A comparison of several parameters of 73 published reviews that were based on a search developed with this method to 258 reviews that acknowledged information specialists from other Dutch academic hospitals shows that the performance of the searches following our method is comparable to those performed in other institutes but that the time needed to develop the search strategies was much shorter than the time reported for the other reviews [ 9 ].

CONCLUSIONS

With the described method, searchers can gain confidence in their search strategies by finding many relevant words and creating exhaustive search strategies quickly. The approach can be used when performing SR searches or for other purposes such as answering clinical questions, with different expectations of the search’s precision and recall. This method, with practice, provides a stepwise approach that facilitates the search strategy development process from question clarification to final iteration and beyond.

SUPPLEMENTAL FILE

Acknowledgments.

We highly appreciate the work that was done by our former colleague Louis Volkers, who in his twenty years as an information specialist in Erasmus MC laid the basis for our method. We thank Professor Oscar Franco for reviewing earlier drafts of this article.

Literature Reviews

• Types of reviews
• Getting started

Types of reviews and examples

Choosing a review type.

• 1. Define your research question
• 2. Plan your search
• 3. Search the literature
• 4. Organize your results
• 5. Synthesize your findings
• 6. Write the review
• Artificial intelligence (AI) tools
• Thompson Writing Studio This link opens in a new window
• Need to write a systematic review? This link opens in a new window

Contact a Librarian

Ask a Librarian

• Meta-analysis
• Systematized

Definition:

"A term used to describe a conventional overview of the literature, particularly when contrasted with a systematic review (Booth et al., 2012, p. 265).

Characteristics:

• Provides examination of recent or current literature on a wide range of subjects
• Varying levels of completeness / comprehensiveness, non-standardized methodology
• May or may not include comprehensive searching, quality assessment or critical appraisal

Mitchell, L. E., & Zajchowski, C. A. (2022). The history of air quality in Utah: A narrative review.  Sustainability ,  14 (15), 9653.  doi.org/10.3390/su14159653

Booth, A., Papaioannou, D., & Sutton, A. (2012). Systematic approaches to a successful literature review. London: SAGE Publications Ltd.

"An assessment of what is already known about a policy or practice issue...using systematic review methods to search and critically appraise existing research" (Grant & Booth, 2009, p. 100).

• Assessment of what is already known about an issue
• Similar to a systematic review but within a time-constrained setting
• Typically employs methodological shortcuts, increasing risk of introducing bias, includes basic level of quality assessment
• Best suited for issues needing quick decisions and solutions (i.e., policy recommendations)

Learn more about the method:

Khangura, S., Konnyu, K., Cushman, R., Grimshaw, J., & Moher, D. (2012). Evidence summaries: the evolution of a rapid review approach.  Systematic reviews, 1 (1), 1-9.  https://doi.org/10.1186/2046-4053-1-10

Virginia Commonwealth University Libraries. (2021). Rapid Review Protocol .

Quarmby, S., Santos, G., & Mathias, M. (2019). Air quality strategies and technologies: A rapid review of the international evidence.  Sustainability, 11 (10), 2757.  https://doi.org/10.3390/su11102757

Grant, M.J. & Booth, A. (2009). A typology of reviews: an analysis of the 14 review types and associated methodologies.  Health Information & Libraries Journal , 26(2), 91-108. https://www.doi.org/10.1111/j.1471-1842.2009.00848.x

Developed and refined by the Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI-Centre), this review "map[s] out and categorize[s] existing literature on a particular topic, identifying gaps in research literature from which to commission further reviews and/or primary research" (Grant & Booth, 2009, p. 97).

Although mapping reviews are sometimes called scoping reviews, the key difference is that mapping reviews focus on a review question, rather than a topic

Mapping reviews are "best used where a clear target for a more focused evidence product has not yet been identified" (Booth, 2016, p. 14)

Mapping review searches are often quick and are intended to provide a broad overview

Mapping reviews can take different approaches in what types of literature is focused on in the search

Cooper I. D. (2016). What is a "mapping study?".  Journal of the Medical Library Association: JMLA ,  104 (1), 76–78. https://doi.org/10.3163/1536-5050.104.1.013

Miake-Lye, I. M., Hempel, S., Shanman, R., & Shekelle, P. G. (2016). What is an evidence map? A systematic review of published evidence maps and their definitions, methods, and products.  Systematic reviews, 5 (1), 1-21.  https://doi.org/10.1186/s13643-016-0204-x

Tainio, M., Andersen, Z. J., Nieuwenhuijsen, M. J., Hu, L., De Nazelle, A., An, R., ... & de Sá, T. H. (2021). Air pollution, physical activity and health: A mapping review of the evidence.  Environment international ,  147 , 105954.  https://doi.org/10.1016/j.envint.2020.105954

Booth, A. (2016). EVIDENT Guidance for Reviewing the Evidence: a compendium of methodological literature and websites . ResearchGate. https://doi.org/10.13140/RG.2.1.1562.9842 . 

Grant, M.J. & Booth, A. (2009). A typology of reviews: an analysis of the 14 review types and associated methodologies.  Health Information & Libraries Journal , 26(2), 91-108.  https://www.doi.org/10.1111/j.1471-1842.2009.00848.x

"A type of review that has as its primary objective the identification of the size and quality of research in a topic area in order to inform subsequent review" (Booth et al., 2012, p. 269).

• Main purpose is to map out and categorize existing literature, identify gaps in literature—great for informing policy-making
• Search comprehensiveness determined by time/scope constraints, could take longer than a systematic review
• No formal quality assessment or critical appraisal

Learn more about the methods :

Arksey, H., & O'Malley, L. (2005) Scoping studies: towards a methodological framework.  International Journal of Social Research Methodology ,  8 (1), 19-32.  https://doi.org/10.1080/1364557032000119616

Levac, D., Colquhoun, H., & O’Brien, K. K. (2010). Scoping studies: Advancing the methodology. Implementation Science: IS, 5, 69. https://doi.org/10.1186/1748-5908-5-69

Example : 

Rahman, A., Sarkar, A., Yadav, O. P., Achari, G., & Slobodnik, J. (2021). Potential human health risks due to environmental exposure to nano-and microplastics and knowledge gaps: A scoping review.  Science of the Total Environment, 757 , 143872.  https://doi.org/10.1016/j.scitotenv.2020.143872

A review that "[compiles] evidence from multiple...reviews into one accessible and usable document" (Grant & Booth, 2009, p. 103). While originally intended to be a compilation of Cochrane reviews, it now generally refers to any kind of evidence synthesis.

• Compiles evidence from multiple reviews into one document
• Often defines a broader question than is typical of a traditional systematic review

Choi, G. J., & Kang, H. (2022). The umbrella review: a useful strategy in the rain of evidence.  The Korean Journal of Pain ,  35 (2), 127–128.  https://doi.org/10.3344/kjp.2022.35.2.127

Aromataris, E., Fernandez, R., Godfrey, C. M., Holly, C., Khalil, H., & Tungpunkom, P. (2015). Summarizing systematic reviews: Methodological development, conduct and reporting of an umbrella review approach. International Journal of Evidence-Based Healthcare , 13(3), 132–140. https://doi.org/10.1097/XEB.0000000000000055

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A systematic review "seeks to systematically search for, appraise, and [synthesize] research evidence, often adhering to the guidelines on the conduct of a review" provided by discipline-specific organizations, such as the Cochrane Collaboration (Grant & Booth, 2009, p. 102).

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A conceptual framework proposed through literature review to determine the dimensions of social transparency in global supply chains

• Published: 16 May 2024

Cite this article

• Preethi Raja 1 &
• Usha Mohan   ORCID: orcid.org/0000-0003-2161-7600 1  

The current focus in supply chain management (SCM) research revolves around the relationship between sustainability and supply chain transparency (SCT). Despite the three pillars of sustainability – environmental, social, and economic- the limited and scattered analysis is on the social part, and the least is on socially responsible supply chain management (SR-SCM). SCT plays a significant role in elevating the sustainability of the supply chain. This review paper emphasizes the integration of SCT and sustainable supply chain, especially the social aspect as SR-SCM, and coining the new term social transparency (ST). ST is openness to communicating details about the impact of business on people, their well-being, and compliance with social sustainability standards and policies. This paper establishes a conceptual framework using three research methods. systematic literature review, content analysis-based literature review, and framework development. By locating studies in databases like EBSCO, Scopus, and Web of Science, 273 peer-reviewed articles were identified in the intersection of social sustainability, supply chains, and transparency. Finally, the framework proposes five dimensions: tracking and tracing suppliers till provenance, product and process specifications, financial transaction information, social sustainability policies and compliance, and performance assessment to determine ST in global supply chains.

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Data availability

The data that supports the findings of this systematic literature review and content analysis are either included in this manuscript or are publicly available in the referenced sources. All included studies and their respective citations are provided in the reference section. Any additional data or materials used for this review can be obtained upon request from the corresponding author.

Abbreviations

Supply chain management

Socially responsible supply chain management

Supply Chain Transparency

Social Transparency

Multinational Corporations

Code of Conduct

Corporate Social Responsibility

Preferred Reporting Items for Systematic Reviews and Meta-analyses

Radio frequency Identification

Internet of Things

Sustainable Supply Chain Management

Supply Chain

Textile Standard Certification

Worldwide Responsible Accredited Production

Global Organic Textile Standard

Global Recycled Standard

Registration, Evaluation, Authorization and Restriction on the use of Chemicals

Social Accountability International Certification

Indian Standards Institution Mark

Bureau of Indian Standards

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Raja, P., Mohan, U. A conceptual framework proposed through literature review to determine the dimensions of social transparency in global supply chains. Manag Rev Q (2024). https://doi.org/10.1007/s11301-024-00440-1

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SYSTEMATIC REVIEW article

A bibliometrics review of the journal mindfulness : science mapping the literature from 2012 to 2022.

• Department of Education and Learning Technology, National Tsing Hua University, Hsinchu, Taiwan

This study conducts a bibliometric analysis using the Web of Science database on 1,950 articles published in the journal Mindfulness from 2012 to 2022. By constructing a knowledge graph, the research delineates the evolution, stages of development, and emerging trends in the field of mindfulness. Significant growth in the annual publication volume has been observed since 2012, with the research progression segmented into three distinct phases. The United States has emerged as a pivotal contributor to the field, dominating in terms of publication volume, researcher involvement, and institutional contributions. Through the application of keyword co-occurrence and reference co-citation analysis, five principal clusters were identified, focusing on mindfulness, meditation, depression, stress, and self-compassion, underscoring these as focal research areas. Furthermore, the exploration of mindfulness within the educational sphere in Taiwan is still nascent, signaling a critical need for bolstered research support in diverse thematic domains.

Introduction

Mental health, a cornerstone of contemporary society, substantially influences national productivity and interpersonal relationships. Recent advances in mindfulness research suggest that cultivating mindfulness fosters positive and resilient attitudes towards evolving social contexts. Kabat-Zinn’s pioneering work in 1979 integrated Buddhist mindfulness meditation into the medical realm, inaugurating Mindfulness-Based Stress Reduction (MBSR) clinics—a milestone in healthcare applications of mindfulness ( Wen, 2016 ).

As research into mindfulness has deepened, its applications have broadened from medicine to fields such as psychology, education, business, and even commercialization ( Wilson, 2014 ). Empirical studies indicate that mindfulness enhances self-acceptance, care, and courage, which in turn uplifts the quality of life ( Dobkin and Zhao, 2011 ). Additionally, it mitigates symptoms of stress, depression, and anxiety, bolsters positive emotions, and improves psychological health ( Keng et al., 2011 ; Lakhan and Schofield, 2013 ; Penman and Burch, 2013 ). It also positively impacts attention and emotional regulation ( Wen, 2013 ), and strengthens interpersonal relationships ( Grossman et al., 2004 ; Davis and Hayes, 2011 ). As a holistic approach to psychological and physical well-being, mindfulness education encourages students to deepen their reflective thinking, enhance awareness, and apply mindfulness practices, thus continually advancing their academic and health outcomes. For instance, Napoli et al. (2005) observed that mindfulness improves children’s selective attention, mental health, and cognitive functions, enhancing overall well-being. Mindfulness interventions have also been shown to mitigate depressive symptoms in adolescents ( Raes et al., 2014 ; Kallapiran et al., 2015 ) and to enhance cognition, academic performance, behavior, and socio-emotional qualities among primary and secondary students ( Maynard et al., 2017 ). Hsieh (2018) advocates for the integration of mindfulness into school education and management, aiming to foster a comprehensive understanding of life’s significance, the pursuit of meaningful values, and the promotion of care and social responsibility.

In Taiwan, mindfulness research, albeit more recent, focuses on enhancing attention, body and mind awareness, emotional processing, and stress regulation through mindfulness practices, or explores its benefits in physical and mental health, professional development, and patient care ( Chen et al., 2019 ). Thus, it is necessary for Taiwan to further expand the application domains of mindfulness research and to support its development through government policies, as well as to strengthen interdisciplinary collaborations to deepen the understanding of mindfulness effects across various groups. For example, Jin and Liu (2017) implemented targeted mindfulness interventions for special student groups, providing insightful experiences applicable to broader student populations; Jiang et al. (2022) integrated insights from psychology, education, and sociology to explore how mindfulness parenting positively affects parent–child relationships and alleviates behavioral issues in children, contributing significantly to societal welfare.

The advent of Knowledge Graph technology has revolutionized the exploration of disciplines, academic communities, and intellectual traditions through the analysis of journal articles. Unlike traditional reviews and meta-analyses, bibliometric analysis offers a detailed summary of a field’s literature metrics and knowledge structure by examining the structural relationships among authors, countries, institutions, and themes, employing statistical methods such as article counts, reference co-citation analysis, and impact factors ( Donthu et al., 2021 ). This approach provides a more nuanced understanding of the dynamics across various scientific fields, enhancing the scope and depth of academic exploration.

To date, the systematic construction of knowledge graphs in the realm of mindfulness research remains limited. The journal Mindfulness serves as a critical resource for advancing the assessment, prevention, treatment, counseling, training, and collaboration of mindfulness theories and interdisciplinary studies. Given this backdrop, a comprehensive analysis of the mindfulness research literature is essential. This analysis will facilitate a macroscopic understanding of the developmental trajectory, knowledge base, research hotspots, and future research directions in this field. Moreover, it will inform recommendations and enhancements for education in Taiwan.

This paper employs VOSviewer (v.1.6.18) to analyze mindfulness-related research from 2012 to 2022, exploring thematic developments and presenting the findings via a knowledge graph, providing a foundational reference for future studies. This study addresses the following research questions:

1. What is the publication count and growth trajectory of mindfulness literature?

2. Which authors and countries have the most significant influence on mindfulness research?

3. What are the primary research hotspots within the field of mindfulness?

4. What implications does mindfulness research hold for the educational in Taiwan?

Literature review

“Mindfulness,” often associated with terms such as contemplation, introspection, and concentration, originates from the Buddhist term “sammā-sati,” which translates to “Right Mindfulness” or simply “Mindfulness.” This term encapsulates the concepts of awareness, attention, and remembering, essential for alleviating physical and mental stress ( Lv, 2014 ). Buddha, who lived approximately 2,500 years ago, emphasized that mindfulness is crucial for overcoming ‘attachment, aversion, and delusion.’

The theoretical foundation of mindfulness research was laid by Ellen Langer, a social psychologist at Harvard University. In her 1989 work, she proposed that many negative life outcomes, such as unhappiness, accidents, and poor health, could result from a lack of mindfulness ( Langer, 1989 ). Thus, she viewed mindfulness as both a method of mental training and a way of life, helping individuals to observe changes within their bodies and minds and to maintain an open, accepting, and clear presence in the moment ( Lin, 2013 ).

Mindfulness, deeply rooted in religious traditions, has evolved significantly under their influence. Wen (2013) emphasized that mindfulness focuses on present awareness and mental states, which profoundly impact human physical and mental health. The process involves causal interactions that construct what is termed “experience,” generated through the senses (eye, ear, nose, tongue, body, mind) and integrated conceptually. Mindfulness categorizes the six senses into five aggregates: form (material), sensation, perception (identification and evaluation), volitional formations (responses and actions), and consciousness. The practice asserts that identification with “self,” “mine,” or “myself” is illusory, and true awareness is based on this realization. Buddha taught that mental changes are constant and recognizing this allows for greater flexibility and acceptance in responding to life’s changes ( Ronald et al., 2009 ).

Mindfulness has been extensively researched within medicine, modern psychology, and social psychology, influenced initially by psychologist Kabat-Zinn. In 1979, he introduced the Mindfulness-Based Stress Reduction (MBSR) technique, applying mindfulness to clinical psychology with a focus on emotional regulation, stress management, mind–body interaction, and meditation practices. Numerous studies have confirmed mindfulness’s effectiveness in alleviating physical and psychological distress ( Kabat-Zinn, 2003 ). Recent research indicates positive effects of mindfulness interventions on individuals with amphetamine-type substance use disorders (SUDs), highlighting improved mindful awareness and certain electroencephalographic functional connectivity ( Zhang et al., 2019 ). Additionally, a meta-analysis of 40 randomized controlled trials on mindfulness-based interventions (MBIs) for SUDs, excluding tobacco use disorders, suggests these interventions might slightly reduce substance use days compared to standard care, cognitive-behavioral therapy, or pharmacotherapy, though further research is needed to confirm their overall effectiveness ( Goldberg et al., 2021 ). MBIs have also been successfully applied to a range of addictions, from smoking to alcohol, and behavioral addictions like gambling disorders, reducing dependency, cravings, and improving emotional states. Common MBI methods include Mindfulness-Based Relapse Prevention, Mindfulness Training for Smokers, and Mindfulness-Oriented Recovery Enhancement, with the integration of MBIs with treatment as usual (TAU) or other active treatments proving most effective ( Sancho et al., 2018 ). MBSR courses have not only benefitted the fields of medicine, psychology, and education but have also been widely promoted within the corporate sector, significantly improving physical and mental health, emotions, and quality of life ( Hsieh, 2019 ). Research by Valentine and Sweet (1999) showed that mindfulness meditators exhibit better psychological health than those practicing focused meditation. Various studies have explored the attention mechanisms of mindfulness meditation, correlating it with mental health improvements through attention regulation, body awareness, emotional regulation, and changing self-perceptions. Evidence suggests mindfulness meditation training enhances attention-related behavioral responses, cognitive abilities, reduces stress, and increases well-being ( Jha et al., 2007 ; Chiesa et al., 2011 ; Hölzel et al., 2011 ; Eberth and Sedlmeier, 2012 ; Jensen et al., 2012 ).

Research on self-compassion, particularly prevalent in Western studies, highlights its components—self-love, reduced self-judgment, decreased feelings of isolation, mindfulness, and lessened over-identification. Self-compassion interventions foster self-care, kindness, and tolerance, aiding individuals, especially the youth, in developing positive internal processing systems and reducing mental health issues. Its core aspects include treating oneself kindly, recognizing common humanity, and maintaining mindfulness ( Neff, 2003a ; MacBeth and Gumley, 2012 ; Körner et al., 2015 ; Costa et al., 2016 ; Muris et al., 2016 ; Neff et al., 2017 , 2019 ). Additionally, mindfulness regulates emotions, enhances attention, reduces stress, and positively impacts interpersonal communication and creativity ( Grossman et al., 2004 ; Corcoran et al., 2010 ; Farb et al., 2010 ; Davis and Hayes, 2011 ; Keng et al., 2011 ; Lakhan and Schofield, 2013 ; Lawlor, 2014 ; Penman and Burch, 2013 ; Wall, 2014 ; Willis and Dinehart, 2014 ; Laukkonen et al., 2020 ).

Compared to Western studies, mindfulness research in Taiwan shows distinct traits. In quantitative studies, there is a strong focus on developing mindfulness scales, therapeutic interventions, and curriculum implementation. For example, Huang et al. (2015) conducted reliability and validity analyses of the “Taiwanese Version of the Five Facet Mindfulness Questionnaire”; Liu and Rau (2015) investigated how mindfulness meditation enhances attention; Yang (2016) integrated mindfulness practices into curricula and assessed impacts through pre- and post-tests using the “Five Facet Mindfulness Questionnaire,” “Stress Perception,” and “Mindfulness Attention Awareness Scale.” In qualitative research, studies often focus on specific benefits or challenges encountered during mindfulness practices. For instance, Zheng et al. (2013) examined the effects of adult mindfulness courses on depression, anxiety, and mindfulness awareness, finding no significant differences; Shin and Jin (2010) discussed how “Zen Mindfulness Groups” influence intern counselors’ self-focus and professional practices. These studies provide insights into the effects of mindfulness on specific target groups and contribute to a deeper understanding of factors influencing mindfulness practices.

The ongoing deepening of mindfulness practice enables scholars to gain profound insights into their behavioral and cognitive patterns, reflecting on and adjusting their values and beliefs. This integration of awareness and action not only advances research in mindfulness but also demonstrates its significant applicative value across various fields such as medicine, psychology, and education, effectively enhancing individual well-being and broader societal impact.

In recent years, bibliometric analysis has emerged as a fundamental method in scientific research, providing quantitative and statistical evaluation of scholarly outputs such as journal articles, citation counts, and impact factors ( Donthu et al., 2021 ). First introduced by Pritchard in 1969, the concept of bibliometrics pertains to the systematic analysis of scholarly literature to understand the evolution and structural dynamics of academic disciplines ( Pritchard, 1969 ). This review applies bibliometric techniques to scrutinize significant literature and themes within the field of mindfulness research, aiming to delineate the current state of the discipline and project future research directions.

The analysis utilizes VOSviewer (version 1.6.18) as the principal tool, capitalizing on its ability to create knowledge maps that visualize relationships between various bibliometric elements. These include descriptive analysis, examination of authorship and geographical distribution, keyword co-occurrence, and reference co-citation analyses. VOSviewer is renowned for its effectiveness in graphically representing scientific landscapes, thereby facilitating the exploration of connections across diverse research areas ( Van Eck and Waltman, 2010 ; Zupic and Čater, 2015 ).

Keyword co-occurrence analysis is particularly valuable for detecting research development trends and assessing the status of domains ( Zhang, 2013 ; Yang, 2015 ). In this analysis, keywords with higher co-occurrence frequencies are indicative of prevailing research hotspots, highlighting the central themes within the field. This method employs visual representations of co-occurrence networks, where nodes represent keywords, encapsulating the cumulative knowledge of a domain, and links illustrate the relationships between word pairs, denoting their co-occurrence ( Radhakrishnan et al., 2017 ).

Reference co-citation analysis is employed to measure the similarity between documents or topics based on the frequency of their co-citations ( Small, 1973 ). The density of connection lines in the co-citation network graphically represents the strength of relationships between documents, providing insights into the interconnectedness of research themes. This type of analysis is crucial for identifying topics that have gained traction in the short term and may also indicate emerging research frontiers ( Zhang, 2013 )

Data source, procedure, and analytic software

This study employs data sourced from the Web of Science (WoS) Core Collection, which includes the Social Sciences Citation Index (SSCI), Science Citation Index Expanded (SCI-Expanded), and Arts & Humanities Citation Index (A&HCI). These databases are recognized for their extensive reach and integration across multiple disciplinary areas, holding significant academic influence ( Zyoud et al., 2017 ). WoS is particularly noted for its comprehensive coverage, with approximately 99.11% of its indexed journals also featured in the Scopus database, underscoring its broad applicability and prominence in global research landscapes ( Singh et al., 2021 ). The journal Mindfulness , indexed in the SSCI and ranking highly within the Psychiatry and Clinical Psychology categories, consistently achieves Q1 and Q2 status, indicative of its high-quality scholarly output. Thus, the selection of research papers from these sources ensures a reliable representation of the mindfulness research quality, supporting the validity of the study’s findings.

Bibliometric analysis serves as a crucial tool for elucidating the accumulated scientific knowledge and developmental nuances of established fields through the systematic examination of large volumes of unstructured data ( Donthu et al., 2021 ). This study adopts the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework ( Figure 1 ), guiding the systematic literature review process to ensure transparency and standardization in the bibliometric methodology. This approach aids in the precise selection of relevant outcomes ( Moher et al., 2009 ). This study specifically focuses on articles from the Mindfulness journal indexed in the WoS database, covering the period from 2012 to 2022. The selected articles encompass a wide array of types, including academic papers, conference proceedings, editorial materials, book reviews, and chapters. These documents collectively address diverse aspects of mindfulness, including therapy and intervention measures tailored to different populations, and explore various research directions such as the application of mindfulness in different therapeutic contexts and intervention strategies. After removing duplicates and irrelevant entries, a search conducted in December 2022 resulted in a corpus of 1,950 documents ( Figure 1 ), forming the basis for subsequent bibliometric analyses.

Figure 1 . Flow diagram of study selection process.

The extracted data includes authors, paper titles, and keywords, which were inputted into the VOSviewer software for visual mapping. This software supports the comparison of normalized cluster networks, coverage visualization, and density visualization ( Van Eck and Waltman, 2010 ), allowing for comprehensive bibliometric analysis through appropriately set threshold values.

Results and discussion

Yearly quantitative distribution of literature.

As shown in Figure 2 , the journal Mindfulness has published a total of 1,950 articles in the WoS database as of December 2022. Since its inception in 2012, the annual publication volume has exhibited a consistent upward trajectory, delineated into three distinct stages: the “Emergence Stage” (2012–2014), where fewer than 100 articles were published each year; the “Exploration Stage” (2015–2018), characterized by a gradual increase in publication numbers, with 2015 marking the first year the journal exceeded 150 articles; and the “Growth Stage” (2019–2022), noted for a robust and stable trend of publishing over 200 articles annually starting in 2019. This latter stage underscores a burgeoning interest in mindfulness research. Nonetheless, there was a notable decline in publication numbers in 2021 and 2022, a trend likely influenced by the global disruptions caused by the COVID-19 pandemic.

Figure 2 . Yearly quantitative distribution of literature.

Significant publications in different development stages

Table 1 categorizes key literature from the journal Mindfulness into three developmental stages, highlighting the impact of these works through the lens of “the top three most-cited articles” in the WoS database. This method underscores the relevance and significance of these articles within their respective research domains.

Table 1 . Significant publications in different development stages.

During the “Emergence Stage” (2012–2014), 183 articles were published. The top three most-cited articles included Eberth and Sedlmeier (2012) , Meiklejohn et al. (2012) , and Sauer et al. (2013) . Eberth and Sedlmeier (2012) offered a comprehensive review of the effects of mindfulness meditation on various psychological variables among non-clinical meditators. Meiklejohn et al. (2012) explored the integration of mindfulness training into K-12 curricula, employing a combination of direct and indirect teaching methods facilitated by teacher training. This study highlighted that continuous mindfulness practice enhances attention and emotional regulation, benefiting both teachers and students. Sauer et al. (2013) emphasized the necessity of comparing mindfulness measurement results obtained through self-assessment tools with those from other mindfulness measurement tools, providing insights for improving current methodologies.

The “Exploration Stage” (2015–2018) produced 687 articles, with Zoogman et al. (2015) , Neff (2016) , and Tomlinson et al. (2018) being the most cited. Neff (2016) introduced the Self-Compassion Scale (SCS), establishing it as an effective measure of self-compassion and highlighting the importance of the “self-criticism” factor. Zoogman et al. (2015) investigated mindfulness-based interventions for adult depression and anxiety, suggesting potential applicability to adolescents, especially in non-clinical settings. Tomlinson et al. (2018) examined the correlation between trait mindfulness and mental health, indicating positive impacts and pointing out areas for future research, including addressing conceptual and methodological challenges in the field.

From 2019 to 2022, the “Growth Stage” saw the publication of 1,080 articles, with significant contributions from Ferrari et al. (2019) , Flett et al. (2019) , and Wilson et al. (2019) . Ferrari et al. (2019) validated the effectiveness of self-compassion interventions in enhancing psychosocial outcomes. Flett et al. (2019) explored both the short-term and long-term benefits of mindfulness meditation on mental health. Wilson et al. (2019) reviewed therapies related to self-compassion, including compassion-focused therapy and mindfulness-based cognitive therapy, demonstrating significant improvements in conditions like anxiety and depression, thus promoting self-compassion and reducing psychopathology among both clinical and subclinical populations.

In summary, each developmental stage of Mindfulness research progressively explores different facets, with a significant emphasis on the management and regulation of psychological processes like self-regulation, emotions, and psychological health, which are increasingly recognized as central themes in contemporary mindfulness research.

Distribution of authors

As shown in Figure 3 , this study’s analysis of author distribution provides insights into their connections with international scholars. Among the 200 authors featured on Mindfulness, notable contributors include Kabat-Zinn, Analayo, Van Gordon, Medvedev and Bögels.

Figure 3 . Distribution of authors.

In 1979, Dr. Kabat-Zinn launched the MBSR program at the University of Massachusetts Medical School, effectively helping patients handle stress, pain, and illness through mindfulness techniques. His method, practiced in over 200 medical institutions across North America, has significantly influenced healthcare, education, and other sectors for decades. Dr. Kabat-Zinn’s numerous publications, including Full Catastrophe Living and The Mindful Way Through Depression, have further popularized these approaches ( Kabat-Zinn, 2023 ).

Dr. Analayo of the Barre Center for Buddhist Studies and the Numata Centre for Buddhist Studies at The University of Hamburg focuses on early Buddhist texts and meditation practices. His work bridges ancient Buddhist techniques with modern practices, exploring mindfulness as a connection between mind and body, vital for continuous awareness in daily life ( Anālayo, 2020 ).

Dr. Van Gordon, from the University of Derby, has established credibility in studying the efficacy of Buddhist-derived meditations like Loving-Kindness Meditation (LKM) and Compassion Meditation (CM) in treating a range of mental health problems. His research emphasizes the foundational importance of Meditation Awareness Training (MAT) in enhancing psychological well-being in educational settings among other applications ( University of Derby, 2023 ).

Dr. Medvedev from the University of Waikato has refined the Five Facet Mindfulness Questionnaire using Rasch analysis to enhance its precision and validity, supporting its application in diverse psychological and health-related fields ( The University of Waikato, 2023 ). His research covers various fields, such as assessment methods, health psychology, psychophysiology, and biostatistics.

Dr. Bögels, a professor at the University of Amsterdam, has extensively researched the interplay between cognitive-behavioral therapy and mood disorders in treating childhood social anxiety ( University of Amsterdam, 2023 ). Her findings on the effectiveness of mindful parenting as a therapeutic intervention highlight its benefits in reducing stress and improving family dynamics ( Bögels et al., 2014 ).

As shown in Table 2 , Kabat-Zinn, Analayo, and Van Gordon have predominantly focused on exploring aspects of Buddha’s teachings, the inherent purity of the meditator’s mind, and Meditation Awareness Mindfulness, among other elements. Analayo and Van Gordon bring unique perspectives to their empirical research on meditation’s role in regulating personal physical and mental states, enhancing internal awareness, insight, compassion, and peace. Kabat-Zinn, on the other hand, has been pivotal in integrating mindfulness into psychological therapy and neuropsychology, significantly advancing the therapeutic landscape by mitigating physical and mental distress and promoting overall well-being. Their collective research emphasizes the efficacy of mindfulness interventions in alleviating anxiety and stress, while also advocating for the enhancement of physical and mental health and overall happiness. Medvedev, renowned for his expertise in assessing mindfulness, excels in documenting the observable benefits and self-regulation strategies of mindfulness training through the use of questionnaires, observations, and interviews. In contrast, Bögels concentrates on the application of mindfulness counseling treatment to address stress, depressive mood, and situational trait anxiety among children and their parents, revealing significant benefits in children’s cognition, social interaction, self-care, and mental health.

Table 2 . Distribution of authors.

Distribution of countries and institutions

As shown in Figure 4 , this study analyzes the distribution of publications and institutions to elucidate geographical knowledge networks within the field of mindfulness. An examination of publications from the Mindfulness journal indicates a wide international spread, involving researchers from 68 countries, with the United States, United Kingdom, Canada, Australia, and China being the primary contributors. Among these, significant institutions include the University of Massachusetts System, University of Massachusetts Worcester, Barre Center for Buddhist Studies, University of California System, and University System of Georgia, all located in the USA, underscoring the predominant role of the United States in mindfulness research. Notably, the focus of Chinese research is primarily centered in Hong Kong, signaling its prominence in China’s mindfulness studies, while suggesting that other regions in China could enhance their contributions to this field.

Figure 4 . Distribution of countries and institutions.

As shown in Table 3 , the United States leads in publication volume, followed by the United Kingdom, with substantial inputs from Canada and Australia, whereas China exhibits fewer publications. This distribution underscores a pronounced interest and earlier initiation of mindfulness research among scholars in the US and UK. Institutions like the University of Massachusetts System, Barre Center for Buddhist Studies, and University of Derby, which house principal authors in mindfulness research, are closely aligned with core fields such as mindfulness meditation, training, measurement, intervention, and regulation. This alignment reflects a concentrated and specialized focus in the developmental stages of mindfulness research.

Table 3 . Distribution of countries and institutions.

Keyword co-occurrence

The analysis of keyword co-occurrence in this study is based on the size of network nodes, which represents the importance of each keyword. The larger the keyword, the closer it is to the research hotspot. As shown in Figure 5 , the high-frequency keywords in the Mindfulness journal are ‘mindfulness,’ ‘meditation,’ ‘depression,’ ‘self-compassion,’ and ‘stress,’ all of which are at the core of the clusters.

Figure 5 . Keyword co-occurrence.

As shown in Table 4 , “Mindfulness” is identified as the central term across all articles, reflecting its prevalent usage within the field. The analysis reveals other significant keywords such as “meditation,” “depression,” “self-compassion,” and “stress.” Notably, “meditation” was a dominant theme in the initial stages of research, with a marked increase in related studies between 2014 and 2018, while “self-compassion” gained prominence around 2020. This study organizes these keywords into five distinct clusters based on node size. The red cluster, focusing on “depression,” incorporates themes like anxiety, systematic analysis, cognitive therapy, and treatment, primarily concerning mindfulness treatment. The green cluster, centered around “self-compassion,” includes terms related to emotion regulation, intervention, psychological health, and acceptance, highlighting aspects of mindfulness regulation. The blue cluster, led by “mindfulness,” deals with the facets of examination, questionnaires, psychometric properties, and grading, pertinent to mindfulness assessment. The yellow cluster, under the banner of “meditation,” delves into mechanisms, attention, compassion, and empathy, enriching the discourse on mindfulness meditation. Lastly, the purple cluster, themed around “stress,” addresses issues related to health, adolescents, well-being, and education, underscoring mindfulness education. Collectively, these clusters illustrate the breadth of mindfulness research, showcasing a range of topics from treatment and regulation to assessment and educational applications, reflecting the evolving dynamics and the comprehensive scope of mindfulness as a research field.

Table 4 . Keyword co-occurrence analysis.

The interconnected themes highlighted by these keywords underscore the varied research focus directions of the journal Mindfulness in recent years, reflecting the dynamic evolution of paradigms within mindfulness research. This body of work integrates several core areas, including addressing unhealthy, negative, and adverse emotions through mindfulness-based interventions, exploring strategies for mental health and emotion regulation, conducting evaluations with mindfulness-related questionnaires, investigating meditation practices to foster understanding and empathy, and developing psychophysical educational programs specifically designed for adolescents. Collectively, these focal points illustrate the journal’s commitment to advancing both the theoretical and practical aspects of mindfulness, contributing significantly to our understanding of its diverse applications across various contexts.

Reference co-citation

This study aims to explore the development and dynamic evolution of themes and their relationships within the mindfulness research field, thereby enhancing our understanding of its current state and providing valuable scientific guidance for scholars. As shown in Figure 6 , an analysis of references from the Mindfulness journal reveals that reference co-citations are divided into five clusters: the red cluster focuses on mindfulness assessment with 70 articles, primarily exploring the development and validation of related scales; the green cluster, comprising 65 articles, assesses various mindfulness therapies; the blue cluster, with 62 articles, discusses the structural aspects of mindfulness; the yellow cluster includes 43 articles on mindfulness intervention, evaluating its structural composition and clinical intervention mechanisms; and the purple cluster, consisting of 37 articles, measures the effectiveness of mindfulness across medicine, psychology, education, and other fields. Prominent researchers contributing to these clusters include Kabat-Zinn (2003) , Neff (2003a) , Bishop et al. (2004) , Baer et al. (2006) , and Kabat-Zinn (2009) , whose works significantly shape the discourse within these areas.

Figure 6 . Reference co-citation.

As shown in Table 5 , the article “Using Self-Report Assessment Methods to Explore Facets of Mindfulness” by Baer et al. (2006) stands out as the most strongly linked article, published by the American Psychological Association, Society for Clinical Psychology (Division 12), Section IX (Assessment). This pivotal article investigates various methods and approaches for self-assessing mindfulness. Following closely is “The Benefits of Being Present: Mindfulness and Its Role in Psychological Well-Being” by Brown and Ryan (2003) , which utilized the Mindful Attention Awareness Scale (MAAS) to analyze mindfulness’s predictive and regulatory role in psychological health, published in Personality and Social Psychology. The reference co-citations related to these articles are organized into five clusters that reflect their influence and connections within the field. The red cluster, focusing on mindfulness assessment, is highlighted by Baer et al. (2006) , who discuss the multifaceted nature of mindfulness and its assessment techniques. This cluster emphasizes articles that delve into the development and validation of scales designed to measure mindfulness attributes accurately. The green cluster centers around mindfulness therapy, featuring Hofmann et al. (2010) who confirm the effectiveness of mindfulness therapies in treating clinical issues like anxiety and depression. This cluster collectively examines the therapeutic applications and outcomes of mindfulness-based interventions. In the blue cluster, which addresses the structural aspects of mindfulness, Brown and Ryan (2003) explore the role of mindfulness in enhancing psychological well-being, showcasing its regulatory impact on mental health through empirical studies. The yellow cluster, dedicated to mindfulness interventions, includes Kabat-Zinn (2009) , whose work discusses practical mindfulness applications in dealing with stress, pain, and illness, emphasizing the operational mechanisms and clinical efficacy of mindfulness. Finally, the purple cluster, focusing on the effectiveness of mindfulness, features Neff (2003a) who develops and validates the Self-Compassion Scale, exploring the beneficial effects of self-compassion as part of a mindfulness approach. This cluster explores how mindfulness practices contribute to overall health and education, highlighting their potential in fostering enhanced well-being across various populations. These clusters demonstrate the journal Mindfulness ’s comprehensive coverage of research that spans theoretical explorations to practical applications, reflecting the dynamic and evolving landscape of mindfulness research.

Table 5 . Reference co-citation analysis.

Conclusion and implications

This study employs bibliometric analysis to conduct a visual analysis of research published in the Mindfulness journal, aiming to provide scholars with a relatively objective perspective to grasp the dynamics and future directions of international mindfulness research. The findings indicate that over the past decade, the journal has published 1,950 articles, with publication numbers increasing over time. Among the many contributors, Kabat-Zinn, Analayo, Van Gordon, Medvedev, and Bögels stand out as key figures, with mindfulness research predominantly concentrated in Western countries, particularly the United States and the United Kingdom, which have had the most significant impact on the field. The research primarily focuses on themes such as mindfulness, meditation, depression, stress, and self-compassion. Moreover, the studies are extensively centered around specific aspects of mindfulness, including “intervention,” “therapy,” “regulation,” “assessment,” and “education.” In Taiwan, mindfulness research is relatively underdeveloped; the analysis of this data not only helps identify current research hotspots and gaps but also provides valuable references for researchers in Taiwan, further facilitating the extensive application and in-depth development of mindfulness studies.

We conducted a systematic analysis of various dimensions within mindfulness research, including institutions, nations, individual researchers, and trending topics, thus uncovering key interconnections among these elements. The distribution of these relationships not only maps the trajectory of mindfulness research but also highlights the global imbalance in research capabilities. Particularly, the cultural drivers and relationships between research hotspots, regions, institutions, and individual researchers are crucial as they facilitate collaboration across geographical, disciplinary, and cultural boundaries, which is vital for the global application and dissemination of mindfulness. For instance, Hofmann et al. (2010) have confirmed through their comprehensive analysis that mindfulness therapy positively impacts symptoms of anxiety and depression, a finding that is consistently underscored by frequent references to “depression” and “therapy.” The high-frequency keywords and reference co-citations exhibit a robust linkage pattern, illustrating interrelated connections among these themes. Not only do these connections enrich the existing literature, but they also provide invaluable references for the further development of mindfulness research, highlighting its significance across various psychological and educational settings. Although Asian countries have lesser participation in mindfulness research, their rich history of traditional meditation practices offers substantial untapped potential for future studies. Strengthening collaborations with Western countries can enhance the exchange of knowledge and technologies, bringing fresh perspectives that are essential for advancing the globalization of mindfulness research.

Implications for mindfulness research and education in Taiwan

In Taiwan, mindfulness research is still in its nascent stages, with a notable absence of publications in the international journal Mindfulness , indicative of a lack of systematic research. Scholars in Taiwan are thus encouraged to align with international research trends in mindfulness, enhancing their analytical approaches. There is a strong recommendation for scholars to focus more on demographic groups that could benefit from improved mental health. This involves intensifying global dialogue and exchange between domestic scholars and their international counterparts, which is vital for understanding the structural and developmental nuances of mindfulness research. This approach will facilitate international comparative studies, promote scientific collaboration globally, and provide robust support for individuals in high-pressure work environments. To improve Taiwan’s education directions and aid Taiwanese researchers in thoroughly exploring the development status and trends of the international mindfulness research fields, this study proposes several strategies to accelerate the internationalization of domestic research and discipline construction. These include integrating mindfulness education into curricula at all educational levels to provide students with systematic training in mindfulness practices like meditation, emotion regulation, and concentration; developing mindfulness teacher training programs to enhance educators’ emotional management skills; promoting mindfulness-friendly campuses to foster a respectful, caring, and harmonious learning environment; integrating mindfulness into special education as an auxiliary therapy for students with conditions such as autism and ADHD to enhance their emotion regulation and self-control; and conducting thorough research and assessments of mindfulness education to gauge its impact on students’ learning outcomes, psychological health, and interpersonal relationships, thereby generating empirical evidence to support the expansion of mindfulness education.

Limitations, and suggestions for future research

While this study presents notable findings, it is not without its limitations. The analysis relies exclusively on literature data from the Journal of Mindfulness in the WoS database, lacking empirical field investigations and experimental validation. This focus restricts the breadth of mindfulness-related literature reviewed, as it does not consider contributions from other journals. Future research could benefit from employing a variety of research methods and data sources, integrating themes such as “intervention, therapy, regulation, assessment, education” to expand the exploration of mindfulness applications across diverse domains and populations. Additionally, by prioritizing key terms within the co-occurrence patterns, new research avenues can be identified, which will drive the development of mindfulness research and offer valuable guidance for practical applications and policy formulation.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

C-CH: Writing – review & editing, Supervision. SL: Writing – original draft, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: mindfulness, science mapping, bibliometric analysis, knowledge graph, visualization analysis

Citation: Hsieh C-C and Li S (2024) A bibliometrics review of the journal mindfulness : science mapping the literature from 2012 to 2022. Front. Psychol . 15:1378143. doi: 10.3389/fpsyg.2024.1378143

Received: 29 January 2024; Accepted: 01 May 2024; Published: 17 May 2024.

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Copyright © 2024 Hsieh and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Shun Li, [email protected]

† These authors have contributed equally to this work and share first authorship

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• Systematic Review
• Open access
• Published: 10 May 2024

Cement-augmented locked plate fixation proximal humerus fractures in elderly patient: a systematic review and meta-analysis

• Dong-Yang Li 1 &
• Kun Zhang 1  

BMC Musculoskeletal Disorders volume  25 , Article number:  368 ( 2024 ) Cite this article

127 Accesses

Metrics details

This systemic review and meta-analysis aimed to evaluate the clinical outcomes of proximal humeral fracture in elderly patient fixation using locked plate with or without cement augmentation.

The databases of PubMed, Embase, and Cochrane Library were searched in August 2023 for literature comparing the clinical outcomes of patients with PHFs treated with locked plate alone and locked plate augmented with cement. Data describing study design; level of evidence; inclusion criteria; demographic information; final follow-up; revision rate; implant failure rate; avascular necrosis rate; total complication rate; constant score; and disability of arm, shoulder, and hand (DASH) score were collected.

Eight studies (one randomized-controlled trial and seven observational studies), involving 664 patients, were identified. Compared with locked plates alone, using cement-augmented locked plates reduced the implant failure rate (odds ratio (OR) = 0.19; 95% confidence interval (CI) 0.10–0.39; P  < 0.0001) and total complication rate (OR = 0.45; 95% CI 0.29–0.69; P  = 0.0002) and improved DASH scores (mean difference (MD) = 2.99; 95% CI 1.00–4.98; P  = 0.003). However, there was no significant difference in clinical outcomes, including revision rate, avascular necrosis rate, and constant score.

In this review and meta-analysis, fixation of the PHFs in elderly patients using locked plates with or without cement augmentation has no significant difference in revision rate, but the implant failure and total complication rates may be lesser on using the cement-augmented locked plate for fixation than on using a locked plate alone. Good results are expected for most patients treated with this technique.

Trial registration

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)21 guidelines were followed to conduct this systematic review and meta-analysis and was registered as a protocol in PROSPERO (CRD42022318798).

Peer Review reports

Introduction

Fractures of the proximal humerus account for 5% of all fractures and are the second most common fractures of the upper extremity after distal radius fractures [ 1 ]. While some proximal humeral fractures (PHFs) are either non-displaced or minimally displaced and can be treated non-operatively with good functional results, complex PHFs often occur in older adults, especially women, and surgery is usually required [ 2 , 3 ].

Numerous surgical techniques for the treatment of complex PHFs, such as percutaneous techniques, intramedullary nailing, plating, and arthroplasty, have been established [ 4 , 5 ]. Among these, open reduction and internal fixation with locked plates have gained wide acceptance and have shown good clinical outcomes [ 6 , 7 , 8 ]. But still not reached desirable levels, the reported complication rates and revision rates remain high [ 9 , 10 , 11 ]. In a multicenter, prospective study by Brunner et al. [ 12 ], 13.8% of cases required revision surgery following mechanical failure. In another study by Königshausen et al., complication rates of up to 23% were reported [ 13 ]. The presence of osteoporotic bone in the older patient population makes screw anchorage difficult. A lack of medial support has been identified as the main cause of treatment failure [ 14 , 15 ]. Considering the above-mentioned factors, the ideal implant should not only make the construct flexible enough to unload the bone-implant interface but also rigid enough to minimize fracture movements [ 16 ].

To enhance stability in internal fixation and to avoid implant failure requiring revision surgery, several techniques have been tested. (For healthy patients under 60 years of age, plates are usually used alone. For patients over 60 years of age with known osteoporosis and decreased bone mineral density, fracture augmentation may be an option.) [ 14 ]. Autograft and allografts with the purpose of addressing the need for medial support and fill the void after osteoporotic fractures. Although it has achieved good clinical results, its limitations limit its wide use. 30 Mechanical devices can also be used for augmentation, despite different design, share similar biomechanical principles. In the literature, two systems have been used in the setting of PHFs: (1) The Da Vinci System or “triangular block bridge”, and (2) The Proximal Humerus Cage or “intramedullary cage”. These implants aim to provide structural support to the humeral head and fill the metaphyseal void. The most commonly described techniques were, in fact, reinforcing the screw-bone interface with cement. There are many kinds of bone cement, Polymethylmethacrylate (PMMMA), calcium phosphate and calcium sulfate, their respective characteristics are different [ 18 , 19 ]. Cement augmentation technique is also slightly different around the word. The most commonly employed technique involves the utilization of cannulated screws for fracture fixation to the plate, then prefilled syringes with PMMA were used to augment cannulated screw with 0.5 to 1 mL of cement for reduction the risk of screw cut-out [ 14 , 17 , 19 ]. Several studies also reported the technique of filling the cement at metaphysical medullary canal around the fracture site [ 19 ].

To analyze the available data, a recent systematic review examined the use of augmentation in the treatment of PHFs. Marongiu et al. [ 19 ] conducted a systematic review of 10 studies that reported the clinical application of cement, bone substitutes, and metallic devices for fracture augmentation in patients with osteoporotic PHFs. The studies included in that systemic review were not sufficient enough to conduct a meta-analysis at that time. Following the publication of the systematic review, two studies comparing fixation with cement-augmented locked plates to locked plates alone in a larger sample size were published.

This review and meta-analysis aimed to analyze the clinical outcomes of cement for fracture augmentation with PHFs in elderly patients.

Materials and methods

(1) Inclusion and exclusion criteria were as follows: 1) Randomized controlled trials (RCTs) or observational studies (OSs), including cohort and case-control studies;2) Comparing the clinical outcomes of using cement-augmented locked plate fixation and without cement-augmented in the management of proximal humerus fractures in elderly patients (Patients over 60 years of age with significant displacement and severe osteoporotic requiring open reduction and internal fixation); 3) a minimum means radiological and clinical follow-up period of 6 months; 4) postoperative surgical data, and functional and radiological outcome data are available. The exclusion criteria were biomechanical studies, computational and finite element analyses, and other nonclinical applications. Moreover, case reports and gray literature were also excluded.

(2) Primary outcomes: Revision and implant failure rates (implant failure in this meta-analysis included loss of reduction, fracture collapse, screw penetration, or screw back-out, as defined and reported by the respective authors).

(3) Secondary outcomes: Avascular necrosis rate, total complication rate (total complications included implant failure, avascular necrosis, wound infection, persistent pain, nerve injury, plate subacromial impingement, and nonunion), the DASH score, and the constant score.

Search Strategy

We searched the databases of PubMed, Embase, and Cochrane Library according to the Cochrane Handbook for Systematic Reviews of Interventions using the following terms: (humeral fracture proximal) AND (bone substitutes OR augmentation OR hydroxyapatite OR cement OR polymethylmethacrylate (PMMA) OR calcium sulfate OR calcium phosphate). The search period was from database creation to August 2023. There were no restrictions in the search process.

Data extraction and quality analysis

Two professional reviewers extracted the data and evaluated the quality of the text in the included articles. Disagreements between the two reviewers were resolved by consensus. The recommendations by Cochrane Handbook for Systematic Reviews of Interventions was used to evaluate the quality of the RCT, including sufficient random sequence generation, allocation concealment, blinding, incomplete result data, selective reporting bias, and other biases. The MINORS criteria [ 20 ] was used to evaluate the quality of the OSs. Items were scored as 0 for not reported, 1 for reported but inadequate, and 2 for reported and adequate. For a comparative study, the ideal score was 24.

Statistical analysis

Review Manager (RevMan, The Cochrane Collaboration, London, United Kingdom) version 5.3 was used for the statistical analysis. We used odds ratios (OR) and mean differences (MD) to present count data and continuous variables, with 95% confidence intervals (CIs). When the statistical heterogeneity between studies was low ( P  > 0.1, I 2  < 50%), the fixed-effects model was used for analysis. In case of high statistical heterogeneity between studies ( P  < 0.1, I 2  > 50%), the possible sources of heterogeneity and possible interference factors were analyzed [ 21 ]. If there was only statistical heterogeneity but no clinical heterogeneity, a random-effects model was used to pool the data. Statistical significance was set at P  < 0.05.

Literature search

The initial database search yielded 712 studies out of which 344 duplicates were excluded. After the preliminary screening, 327 articles were further excluded based on their titles and abstracts. From the remaining 41 studies, the reviewers excluded reviews, protocols, biomechanical studies, and animal studies as per the inclusion and exclusion criteria. Finally, eight studies were included, comprising one RCT and seven case-control studies [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. A flowchart of the process is illustrated in Fig.  1 .

Flowchart of included studies

Baseline information of the included studies

The eight studies had a total of 664 patients at baseline; at study completion, only 635 patients were included in the data analysis (334 patients treated with locked plate alone and 301 with cement-augmented locked plate). Baseline information of the included studies is presented in Table  1 .

Quality Assessment

The RCT used sealed hidden envelopes for allocation but did not specifically mention the blinding that was followed [ 26 ]. On quality assessment of the OSs, three had a total MINORS score of 18 [ 23 , 24 , 29 ]; two studies by Katthagen et al. and Egol et al. had a total MINORS score of 16 [ 25 , 27 ]. Among the other included studies, the study by Foruria et al. had the highest total MINORS score of 19, whereas the lowest total MINORS score of 15 was recorded in a study by Lee et al. [ 22 , 28 ] (Table  2 ).

Outcome measures

Primary outcomes, revision rate.

The revision rate following surgery was the most important outcome analyzed in this systemic review. Three of the eight studies reported revision rates caused by varied reasons, as enumerated in Fig.  2 . Owing to the low heterogeneity, meta-analysis using the fixed-effects model did not reveal any significant difference (OR = 0.56; 95% CI 0.27–1.19; P  = 0.13) in the revision rate between the locked plate only and locked plate augmented with cement groups. We conducted a sensitivity analysis after removing the most weighted study by Hakimi et al. [ 23 ], which showed no significant difference in the number of revision surgeries between the locked plate only and the locked plate augmented with cement groups (OR = 0.91; 95% CI 0.37–2.24; P  = 0.84), with low heterogeneity ( I 2  = 0%).

Forest plot of revision rate in cement and noncemented groups

Foruria et al. reported the various causes for revision surgery in each study group; while most revisions surgeries were due to loss of fixation and plate mechanical interference, the ones due to avascular necrosis were rare [ 22 ]. In a study by Katthagen et al., where only the cement-augmented locked plate group was analyzed, six out of 24 patients (25%) underwent early arthroscopic revision surgery, owing to a limited postoperative range of motion, despite high functional expectations [ 25 ]. Although Egol et al. reported revision surgery in 11 out of 92 patients (12%), their subgroups could not be delineated [ 27 ]. Revision rates were not reported in the remaining three studies either due to shorter follow-up times or successful surgeries.

Implant failure rate

The implant failure rate, another important outcome analyzed in this systemic review, was reported by eight studies. There were 47 reported implant failures in 334 (14%) patients treated with locked plates alone. Only 8 implant failures were recorded in 301 (2.6%) patients treated with the cement-augmented locked plate. There was a statistically significant decrease in the OR for implant failure in the locked plate augmented with cement group compared with that in the locked plate only group (OR = 0.19; 95% CI 0.10–0.39; P  < 0.05), with low heterogeneity ( I 2  = 0%), as shown in Fig.  3 . This suggests that the reliability of fixation using cement-augmented locked plate is higher than that of the locked plate alone.

Forest plot of implant failure rate in cement and noncemented groups

Secondary outcomes

Avascular necrosis rate.

Seven studies assessed the difference in the avascular necrosis rate. There were 13 reported cases of avascular necrosis in 304 (4.2%) patients treated with the locked plate alone, whereas 12 cases of avascular necrosis were recorded in 287 (4.3%) patients treated with cement-augmented locked plate, as depicted in Fig.  4 . We found no significant difference in the incidence of avascular necrosis between the locked plate only and the locked plate augmented with cement groups (OR = 1.01; 95% CI 0.47–2.20; P  = 0.97).Similarly, the heterogeneity in the avascular necrosis rate was low ( I 2  = 0%). In other words, cement augmentation may not increase the risk of avascular necrosis.

Forest plot of avascular necrosis rate in cement and noncemented groups

Total complication rate

Seven out of the eight studies reported the total complication rates in each study group. There were 77 complications reported in 304 (25%) patients treated with the locked plate alone, while 38 complications were noted in 287 (13.2%) patients treated with the cement-augmented locked plate. Owing to the low heterogeneity ( I 2  = 30%), a meta-analysis using the fixed-effects model was conducted, which revealed that fixation with cement-augmented locked plate is associated with a decrease in the total complication rate (OR = 0.45; 95% CI 0.29–0.69; P  = 0.05) (Fig.  5 ).

Forest plot of total complications rate in cement and noncemented groups

Lee et al. [ 28 ] reported the total complication rate in 44 patients, but the number of complications recorded in each group was not specified. Of the 44 patients, 9 (20%) had postoperative complications, with a loss of fixation in five, adhesive capsulitis in three, and deep infection in one.

Three studies used the DASH score to measure functional recovery of the upper limb. We found a difference in heterogeneity ( I²= 16%) between the locked plate only and the locked plate augmented with cement groups (MD = 2.99; 95% CI 1.00–4.98; P  = 0.05) as shown in Fig.  6 . The meta-analysis using the fixed-effects model concluded that patients in the cement-augmented locked plate group may have better functional recovery of the upper limbs. We conducted a sensitivity analysis after removing the most weighted study, by Hengg et al. [ 26 ], which showed no significant difference in DASH score between the locked plate only and the locked plate augmented with cement groups (MD = 1.44; 95% CI 7.27–10.16; P  = 0.75) However, the heterogeneity was relatively high ( I 2  = 51%), indicating that the conclusion may not be reliable.

Forest plot of DASH score in cement and noncemented groups

Constant score

Five studies assessed the difference in constant scores between the locked plate only (256 patients) and cement-augmented locked plate groups (222 patients). The heterogeneity was high ( I 2  = 57%). After verification, no significant clinical heterogeneity was found between the two groups, probably because of differences in the time of postoperative evaluation. The pooled results from the random-effects model suggested no significant difference in the constant score between the two groups (MD = 0.46; 95% CI 3.30–4.21; P  = 0.81), as shown in Fig.  7 . Similarly, the difference was not significant (MD = 1.94; 95% CI 2.36–6.24; P  = 0.38) after removing the most weighted study by Hengg et al. and the heterogeneity was low ( I 2  = 34%) [ 26 ].

Forest plot of constant score in cement and noncemented groups

The main goal of fracture augmentation is to provide mechanical support to osteoporotic bones. In otherwise healthy patients under 60 years of age, plates are usually used alone. The decision to use fracture augmentation is an option for patients over 60 years of age with known osteoporosis and reduced bone density [ 17 ] The most commonly described techniques are auto- and allografting. The concerns about possible autograft-related morbidity in the donor, their availability, and the associated high costs of allograft fixation are problematic to its extensive use [ 30 ]. The application of cement has been proposed as an alternative for the augmentation of osteoporotic PHFs in order to enhance screw anchorage and increase the primary stability of locked plates for displaced PHFs [ 17 , 18 ]. Although good to excellent outcomes of reinforcing the screw-bone interface with cement have been reported in the literature, these options still lack long-term follow-up and large sample comparative studies.

Our meta-analysis included eight papers published from 2009 to 2021 that evaluated the clinical outcomes of 635 older patients with PHFs. The major findings of this meta-analysis are as follows: (1) locked plate with or without cement augmentation for PHFs has the same revision rates, but compared with fixation with locked plate alone, fixation with the cement-augmented locked plate could reduce implant failure and total complication rates; (2) similarly, the cement-augmented locked plate does not increase the risk of avascular necrosis compared with the locked plate alone; (3) cement augmentation effects on clinical functional recovery of the upper limb remain controversial.

According to this meta-analysis, three of the eight studies reported two groups of revision rates for various reasons. There were 22 reported revisions in 198 (11%) patients treated with the locked plate alone, and 11 reported revisions in 165 (6.6%) patients treated with the locked plate augmented with cement. Though cement augmentation can effectively reduce revision rates, the effect is not statistically significant. It is important to note that the implantation of bone cement is generally considered to increase the difficulty and failure rate of secondary revision surgery, in particular arthroplasty. However, Foruria et al. showed that removal of cement augmented screws was technically easy, provided all screws heads had been cleared of cement during the index procedure [ 22 ]. A reduction in the rate of revision is generally associated with a significant reduction in the rate of implant failure. However, in this review, although implant failure rates declined significantly, revision rates did not. This may be related to different expectations from the surgery and shorter follow-up time. In terms of implant failure and total complication rates, treatment with cement-augmented locked plate reduced the implant failure rates from 14 to 2.6% and the total complication rates from 25 to 13%. A locked plate is usually the first choice for the fixation of displaced PHFs. However, owing to osteoporosis in the affected elderly population and the associated difficult screw anchorage, the complication rates are still high. Researchers from the Mayo Clinic reported a 44% total complication rate and a 35% implant failure rate in 2020 [ 31 ]; similar results have been widely reported over the years [ 10 , 32 , 33 ]. Various biomechanical and clinical investigations have been performed to achieve stable implant anchorage in PHFs to enhance the stability in internal fixation and avoid implant failure requiring revision surgery. Kwon et al. conducted biomechanical evaluation with calcium phosphate cement in cadaveric limbs and found that supplementation with calcium phosphate cement led to significant improvements in the mechanical performance of internal fixation [ 34 ]. Röderer et al. showed that screw augmentation could compensate for osteoporotic bones [ 17 ]. Some researchers have reported less movement at the interface between the bone and implant in cement-augmented locked plate osteosynthesis [ 17 , 27 , 35 ]. Almost all in-vitro studies have shown that cement augmentation increases the mechanical strength of the fixation. Our meta-analysis demonstrated that the biomechanical benefits of cement augmentation are clinically applicable without involving additional complications, which could greatly reduce the implant failure and total complication rates. Polymethylmethacrylate (PMMA), the most widely used bone cement, can reach temperatures as high as 100 °C during the polymerization phase, which could potentially cause bone and cartilage necrosis [ 36 ]. The results of our meta-analysis showed a 4.2% humeral head necrosis rate in the group fixated with locked plate alone and 4.1% in the group fixated with cement-augmented locked plate. This finding suggests that PMMA does not increase the probability of humeral head necrosis, which is consistent with the view of some researchers [ 37 , 38 ]. In addition, it should be noted that humeral head necrosis is not only associated with PMMA but may also be associated with the degree of fracture comminution. However, existing clinical studies do not include these data, and further analysis of the impact of cement augmentation on the rates of humeral head necrosis cannot be done.

This meta-analysis used the DASH score and constant score to assess postoperative clinical function. Interestingly, the two criteria produced inconsistent results. On pooling the data from three studies that used the DASH score to assess functional outcomes, it was concluded that cement augmentation may be detrimental to functional recovery [ 23 , 24 , 26 ]. Data from five studies using the constant score to assess functional outcomes did not reveal any significant difference between the cement augmentation group and the locked plate only group; however, the heterogeneity was high [ 22 , 23 , 24 , 25 , 26 ]. This is due to the variable duration of follow-up in the individual studies when calculating the functional score (ideally the studies should have the same follow-up duration). A subgroup analysis can eliminate the heterogeneity seen in this case, provided there are enough studies to be included. Therefore, undertaking further studies could clarify the impact of cement augmentation on functional rehabilitation.

This study has several limitations. First, seven of the included studies were OSs, and only one was an RCT. While the results presented are promising, more RCTs are needed to determine the true efficacy of cement augmentation in the management of PHFs treated with locked plates. Second, the follow-up times in the RCT and OSs included in this review were inconsistent, and the internal design of each study had varying degrees of inadequacy. These factors may affect the authenticity of the results. Third, the potential risk of leakage with cement use has not been mentioned in any of the eight articles, which may underestimate the adverse effect of cement augmentation [ 39 ]. Similarly, the lack of evaluation of radiographic parameters is a significant limitation. Further high-quality RCTs with longer follow-up times, unified measurement standards, and unified radiographic parameters are needed for generating better evidence.

Conclusions

Data availability.

All data generated or analysed during this study are included in this published article.

Abbreviations

proximal humerus fracture

Disability of Arm Shoulder and Hand

randomized controlled trial

observational study

Polymethylmethacrylate

Calcium Phosphate Cement

Calcium Sulfate Cement

mean difference

confidence interval

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Conceived and designed the study: D-Y L, K ZConducted the study: D-Y LAnalyzed the data: D-Y L Wrote the manuscript: D-Y L, K Z.

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Correspondence to Kun Zhang .

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Li, DY., Zhang, K. Cement-augmented locked plate fixation proximal humerus fractures in elderly patient: a systematic review and meta-analysis. BMC Musculoskelet Disord 25 , 368 (2024). https://doi.org/10.1186/s12891-024-07502-1

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Received : 04 December 2023

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DOI : https://doi.org/10.1186/s12891-024-07502-1

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