Last updated:  3 May 2026

What's new in this update

Fulbright 2024 and Premji 2025 were added to the section about translating filters. A new section has been added discussing explorations of alternatives to the use of search filters such as machine classifiers and the use of large language models. Cooper 2025 has been added since it suggests reporting the production of search strategies as case reports. Askin 2025 has been included because of its assessment of inconsistencies in subject indexing when these are assigned automatically or algorithmically.

What are search filters?

Search filters (sometimes called hedges) are collections of search terms designed to retrieve selections of records from a bibliographic database (Jenkins 2004). They may target research using a specific study design (e.g. randomised controlled trial) or topic (e.g. kidney disease) or some other feature of the research question (such as the age of participants). They are usually combined with the results of a subject search using the AND operator. Sometimes filters may be combined with a subject search using NOT to exclude records based on a particular feature (for example to remove animal studies). 

Why would you use a search filter?

When included in a database search strategy, a search filter can reduce the number of records that researchers may need to sift; recent research has shown that this improved search efficiency is a key use of search filters (Beale 2014). The value of search filters can also lie in the fact that they are generated through research, tested and validated, meaning that searchers can benefit from other people’s investment of time and expertise, particularly for broad or challenging topics. Search filters are not available for all study types.

Key features

Filters are typically designed for one purpose, which may be to maximise sensitivity (or recall) or to maximise precision (and thus reduce the number of irrelevant records that need to be screened or assessed for relevance). Sensitivity is the proportion of relevant records retrieved by the filter and is the most frequently reported performance measure (Harbour 2014). Precision is the proportion of relevant records in the retrieved records and is less frequently reported (Harbour 2014). Specificity is the proportion of irrelevant records successfully not retrieved. Performance measures, such as sensitivity and precision, can be difficult to interpret and compare. Alternative graphical approaches to presenting performance information may assist with making decisions about which filter to select (Harbour 2014).

Filters are usually specific to the databases for which they are designed and the interface through which a database is searched. 

The terms used in a study design filter typically include thesaurus (subject index) headings (e.g. Medical Subject Headings (MeSH) for MEDLINE filters) and text words in the title, abstract and author keywords. The reliability of subject indexing continues to be a consideration for filter developers (Askin 2025).  Filters may also feature other available database-specific indexing options such as subheadings or publication types, or other fields dependent on their usefulness for the filter question such as the author address field or journal name.  

Where can you find search filters?

Search filters of interest to researchers producing technology assessments are incorporated into some database interfaces. For example, they are labelled as Clinical Queries in PubMed (https://pubmed.ncbi.nlm.nih.gov/clinical/) and Expert Searches in the Ovid interface (https://tools.ovid.com/ovidtools/expertsearches.html). Often searchers ‘translate’ filters or adapt them to run on different interfaces (Beale 2014, Fulbright 2024, Premji 2025). Translations and adaptations should be undertaken carefully since different interfaces function in different ways, and different databases may have different indexing languages (Glanville 2019).

Study design search filters can also be identified from internet resources such as

• Canada's Drug Agency Search Filters database 

• EAHIL Library of Search Strategy Resources

• the ISSG Search Filter Resource 

• the McMaster Health Information Research Unit Hedges Project website 

• Scottish Intercollegiate Guideline Network (SIGN) search filters 

• searchArxiv

Subject search strategies can be found in various collections. A selected list is provided on the ISSG Search Filter Resource website.

Some guidance documents for the conduct of health technology assessments recommend specific filters and others leave the choice to the discretion of the searcher.

Machine learning classifiers can be characterised as search filters and are increasingly available within systematic review software and as standalone tools.  The ISSG Search Filter Resource provides some examples of machine classifiers.

Critical appraisal of filters

Authors who publish filters should clearly describe the methods they used to compile the filters. It is also valuable to have access to critical assessments of filters that are being used in daily practice. Search filter development methods have developed over time to become more objective and rigorous (Jenkins 2004, Wilczynski 2005).  The quality of a search filter can be appraised using critical appraisal tools (Jenkins 2004, Glanville 2008, Bak 2009) which assess the focus of the filter, the methods used to create it and the quality of the testing and validation which have been conducted to ensure that it performs to a specific level of sensitivity, precision or specificity.

It is also important to know the date when the filter was created so an assessment can be made as to its currency. In some cases, the performance of a search filter may decrease over time as new terms are added to a database thesaurus or as terminology changes.

Search filters are not quality filters in terms of identifying only high quality research evidence. All records resulting from the use of a search filter will require an assessment of relevance and quality. All search filters and all search strategies are compromises and an assessment of the performance of filters for each technology appraisal is recommended.

Increasing numbers of filters have led to the assessment of the relative performance of different filters to find the same study design and these can be a good starting point for deciding which filter to use (Glanville 2009, Beynon 2013). Performance reviews save time since they survey a range of filters and offer an overview of how filters perform, potentially removing the need for a searcher to read many original filter papers. A systematic review of the performance of a large number of diagnostic test accuracy (DTA) filters has recommended that search filters should not be used as the only method for searching for DTA studies for systematic reviews and technology appraisals (Beynon 2013). The review concludes that the filters risk missing relevant studies and do not offer benefits in terms of enhanced precision. A comparison study (Glanville 2009) of the performance of search filters used to identify economics evaluations concluded that, while highly sensitive filters are available, their precision is low. The performance data provided in this paper can help researchers select the filter that’s most appropriate to their needs. A further study (Waffenschmidt 2016) demonstrated that a search filter with adequate precision and sensitivity was not yet available to identify studies of epidemiology in MEDLINE.

Search filter development

Creating a search filter to identify database records of a specific study design or some other feature requires a "gold standard" reference set that can be used to measure performance. The reference set is usually created by using the relative recall approach (Sampson 2006) or by handsearching (Glanville 2008).

A case study (Frazier 2015) describes how a gold standard set was created to support the development of a prognostic filter for studies of oral squamous cell carcinoma in MEDLINE. The methods used are generic and could be applied to both other databases and to other types of research studies. The authors use a flowchart to illustrate the overall process and describe each of the stages.

The authors may have determined the size of their gold standard using a statistical method (Lefebvre 2017).

The authors may have identified the search terms to test in a filter using a variety of methods, sometimes in combination:

• Visual inspection of terms within relevant records

• Text mining approaches

• Talking to experts

Filter authors will usually develop and test their filters using a test set, often a subset of the gold standard reference set, and will validate their filters on a separate validation set of relevant records or in a real world collection of relevant records.

There are recommendations on how to report search filter performance (Lefebvre 2017). 

Recently Cooper 2025 has suggested publishing case reports about search strategy development conducted as part of searches for a systematic review.  Critical appraisal of case reports will be an important consideration in such cases.

Alternatives to search filters

Alternative options to developing and using search filters can be considered. One approach is to carry out sensitive database searches and use the built-in filters or machine learning classifiers that are available in some systematic review software. The classifiers replace the use of study design search filters (Adam 2024, Adam 2025). Moberg 2025 reports a comparison of combining the Cochrane randomized controlled trial (RCT) search filters for Embase and MEDLINE with the Cochrane RCT classifier in the Covidence package, compared to using the classifier alone (Moberg 2025). The authors suggest combining the use of RCT filters with the RCT classifier in Covidence. Other authors have reported a case report suggesting large language models have comparable sensitivity but better precision, compared with an RCT filter (Tran  2025). 

Reference list

Adam 2024

Adam GP, Davies M, George J et al. Machine Learning Tools To (Semi-)Automate Evidence Synthesis: A Rapid Review and Evidence Map. Rockville (MD): Agency for Healthcare Research and Quality (US); 2024.

Adam 2025

Adam GP, Davies M, George J et al. Machine Learning Tools To (Semi-)Automate Evidence Synthesis: A Rapid Review and Evidence Map, 2025. Review Update. 2025. Rockville (MD): Agency for Healthcare Research and Quality (US); 2025.

Askin 2025

Askin, N, Ostapyk, T, Epp, C. Filtering failure: the impact of automated indexing in Medline on retrieval of human studies for knowledge synthesis. Journal of the Medical Library Association. 2025;113.

Bak 2009

Bak G, Mierzwinski-Urban M, Fitzsimmons H, Morrison A, Maden-Jenkins M. A pragmatic critical appraisal instrument for search filters: introducing the CADTH CAI. Health Info Libr J. 2009;26(3):211-219. [Publication appraisal] 

Beale 2014

Beale S, Duffy S, Glanville J, Lefebvre C, Wright D, McCool R, Varley D, Boachie C, Fraser C, Harbour J et al. Choosing and using methodological search filters: searchers' views. Health Info Libr J. 2014;31(2):133-147. [Publication appraisal] 

Beynon 2013

Beynon R, Leeflang MM, McDonald S, Eisinga A, Mitchell RL, Whiting P, Glanville JM. Search strategies to identify diagnostic accuracy studies in MEDLINE and EMBASE. Cochrane Database Syst Rev 2013, Issue 9. [Publication appraisal] 

Cooper 2025

Cooper C, Premji Z, Yavuz C, Engelbert M. Should we adopt the case report format to report challenges in complicated evidence synthesis? A proposal and illustration of a case report of a complex search strategy for humanitarian interventions. Cochrane Evidence Synthesis and Methods. 2025;3:0.

Frazier 2015

Frazier JJ, Stein CD, Tseytlin E, Bekhuis T. Building a gold standard to construct search filters: a case study with biomarkers for oral cancer. J Med Libr Assoc. 2015;103(1):22-30. [Publication appraisal]

Fulbright 2024

Fulbright H A, Stansfield C. Understanding the performance of geographic limits on Web of Science Core Collection databases, using the United Kingdom as an example. J Med Lib Assoc 2024;112(4):332-340

Glanville 2008

Glanville J , Bayliss S, Booth A, Dundar Y, Fernandes H, Fleeman ND, Foster L, Fraser C, Fry-Smith A, Golder S, Lefebvre C, Miller C, Paisley S, Payne L, Price A, Welch K. So many filters, so little time: The development of a Search Filter Appraisal Checklist. J Med Libr Assoc. 2008; 96(4): 356-361. [Publication appraisal]

Glanville 2009

Glanville J, Kaunelis D, Mensinkai S. How well do search filters perform in identifying economic evaluations in MEDLINE and EMBASE. Int J Technol Assess Health Care 2009;25(4):522-529. [Publication appraisal]  

Glanville 2019

Glanville J, Foxlee R, Wisniewski S, Noel-Storr A, Edwards M and Dooley G. Translating the Cochrane EMBASE RCT filter from the Ovid interface to Embase.com: a case study. Health Info Libr J, 2019;36: 264-277.

Harbour 2014

Harbour J, Fraser C, Lefebvre C, Glanville J, Beale S, Boachie C, Duffy S, McCool R, Smith L, Varley D. Reporting methodological search filter performance comparisons: a literature review. Health Info Libr J. 2014;31(3):176-194. [Publication appraisal] 

Jenkins 2004

Jenkins M. Evaluation of methodological search filters - a review. Health Info Libr J. 2004;21:148-163. [Publication appraisal] 

Lefebvre 2017

Lefebvre C, Glanville J, Beale S, Boachie C, Duffy S, Fraser C, et al. Assessing the performance of methodological search filters to improve the efficiency of evidence information retrieval: five literature reviews and a qualitative study. Health Technol Assess 2017;21(69).

Moberg 2025

Moberg K, Gornitzki C. Combining search filters for randomized controlled trials with the Cochrane RCT Classifier in Covidence: a methodological validation study. Research Synthesis Methods. 2025:1-8.

Sampson 2006

Sampson M, Zhang L, Morrison A, Barrowman NJ, Clifford TJ, Platt RW, et al. An alternative to the hand searching gold standard: validating methodological search filters using relative recall. BMC Med Res Methodol. 2006;6(33) [Publication appraisal] 

Tran 2025

Tran V-T, Grana Possamai C, Boutron I, Ravaud P. Using large language models to directly screen electronic databases as an alternative to traditional search strategies such as the Cochrane highly sensitive search for filtering randomized controlled trials in systematic reviews. Research Synthesis Methods. 2025:1-7.

Waffenschmidt 2016

Waffenschmidt S, Hermanns T, Gerber-Grote A, Mostardt S. No suitable precise or optimized epidemiologic search filters were available for bibliographic databases. J Clin Epidemiol. 2016;82:112-18. 

 Wilczynski 2005

Wilczynski NL, Morgan D, Haynes RB; Hedges Team. An overview of the design and methods for retrieving high-quality studies for clinical care. BMC Med Inform Decis Mak. 2005 Jun 21;5:20. [Publication appraisal]