# Getting started with search filters in primary care literature reviews

Thomas Morel, Vera Granikov, Ambar Kulshreshtha, Richard Young, Jean-Pascal Fournier

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## To cite this version:

Thomas Morel, Vera Granikov, Ambar Kulshreshtha, Richard Young, Jean-Pascal Fournier. Getting started with search filters in primary care literature reviews. Family Practice, 2025, 42 (4), 10.1093/fampra/cmaf037. hal-05127071

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Submitted on 3 Mar 2026

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Getting started with search filters in primary care literature reviews

**Running title:** Search filters for primary care reviews

Thomas Morel<sup>a,b</sup>

Vera Granikov<sup>c</sup>

Ambar Kulshreshtha<sup>d</sup>

Richard Young<sup>e</sup>

Jean-Pascal Fournier<sup>a,b</sup>

Corresponding author

Jean-Pascal Fournier, Département de Médecine Générale, Nantes Université, [jean-pascal.fournier@univ-nantes.fr](mailto:jean-pascal.fournier@univ-nantes.fr)

MeSH Terms: Information Science, Systematic review, Methods, Medical subject headings, Databases as Topic

<footnote><sup>a</sup> Nantes Université, Univ Tours, INSERM, MethodS in Patients-centered outcomes and Health Research, SPHERE, F-44000 Nantes, France.</footnote>

<footnote><sup>b</sup> Nantes Université, Département de Médecine Générale, F-44000 Nantes, France</footnote>

<footnote><sup>c</sup> Centre de recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM)</footnote>

<footnote><sup>d</sup> Department of Family and Preventive Medicine, Emory University School of Medicine</footnote>

<footnote><sup>e</sup> Department of Family Medicine, JPS Health Network</footnote>
## Key messages

* Search filters can improve the efficiency of systematic reviews in primary care.

* They help to manage a variety of terminology and improve confidence in research.

* Sensitive and specific filters have different purposes depending on the type of review undertaken.

* Regular updates of search filters are essential to take into account changes in terminology.

## Abstract

Primary care researchers and clinicians are facing an ever-growing evidence base, more options to access research evidence, and increasingly limited time. Incorporating search filters into primary care systematic reviews can significantly improve the efficiency and confidence of the search process. Search filters, or hedges, are pre-developed search strategies that combine controlled vocabulary and free text terms using Boolean operators (words like "AND", "OR"). Search filters help to manage the diverse terminology in the literature, such as the various synonyms for primary care, and can be tailored to the specific needs of the review, whether it aims to be exhaustive or more focused. Resources such as specialized librarians, databases such as PubMed, and repositories such as the InterTASC Information Specialists Sub-Group (ISSG) provide access to these valuable tools. However, as primary care terminology continues to evolve, regular updates to these filters are necessary to maintain their relevance and effectiveness. This method-brief presents search filters and highlights their value for finding research literature in primary care.
**Introduction**

Searching in large bibliographic databases is complex. It is even more complex for primary care research publications that are spread across disciplines, are multidimensional, and may use different terms to describe the primary care setting. Search filters offer a solution.

Search filters, or hedges, are pre-developed search strategies that combine controlled vocabulary and free text terms using Boolean operators (words like “AND”, “OR”). Like diagnostic tests, filters’ performance estimates are measured in terms of sensitivity, specificity, and precision. Search filters are known to be more effective (i.e., less time-consuming) and more reliable (i.e., tested) than traditional searches (1). As suggested in the scoping review by Damarell et al (2019), filters can help minimize inconsistencies and bias (e.g., subjective selection of search terms), overcome individual difficulties in searching (e.g., lack of searching skills or experience), and prevent ‘reinventing the wheel’ (2). Due to these characteristics, filters are commonly used in literature reviews, particularly systematic reviews and guideline development. As the field of primary care continues to grow, rigorous literature reviews are critical to inform practice guidelines and clinical practice.

Systematic reviews are one example. Several methodological guidelines exist to help researchers produce high-quality systematic reviews (3–5), and which recommend identifying evidence through an exhaustive search of several bibliographic databases and grey literature (6). However, searching a bibliographic database containing millions of references is time-consuming and can be challenging to do with accuracy. An ineffective search strategy could, at best, retrieve thousands of irrelevant references, dramatically overwhelming the resources allocated to the systematic review. Worse, it could miss the research question's scope and compromise the review's comprehensiveness. Meanwhile, well-designed and tested search
filters can help produce more focused results, potentially reducing the screening burden for researchers and improving relevance (1). Moreover, in line with the scientific requirements of rigour, transparency, and replicability more objective approaches to searching, such as filters, generate consistent results across searches (7,8). This method-brief explains what search filters are and illustrates their value for conducting more effective and more efficient searches for primary care research.

When to need a search filter: an example in context

The following example illustrates the context in which search filters can be used. A primary care research team is conducting a systematic review to assess the efficacy of proton pump inhibitors (PPIs) when used to treat gastroesophageal reflux disease (GERD) in the primary care context. This team wants to include only randomized controlled trials in the review. The team will first contact their local health librarian. Working with a specialised health librarian is recommended and recognised as valuable, although not always possible (6). The librarian will determine which appropriate databases should be searched to answer the research question and help the team develop a search strategy. The team will inform the search strategy with the core concept of the research question. Although the concepts could be identified by combining the PICO (patient/population/setting, intervention, comparison, and outcomes) model applied to the research question and the study design targeted in the review, it is usually recommended to search only with the health condition (i.e the population), the intervention, and the study design (6,9). In this case, the team will identify that their search strategy should include the concept of GERDs for the population, primary care for the setting, PPIs for the intervention, and randomized controlled trials as the targeted design. Their search strategy will include a query for each concept, combined with the Boolean operator AND (Figure 1). A query, also known as the ‘search string’, is a phrase made up of relevant terms
for a given concept, which is combined to narrow down the results around the concept.

However, creating a complete search strategy of relevant queries takes time and expertise. In our case, the team needs to have a good knowledge of the wording used to describe each core concept in the existing literature. For example, they need to know every synonym for 'primary care use' in a very broad literature.

To optimise time and confidence in designing the search strategy, the health librarian will likely suggest looking at existing search filters for the concepts of primary care and randomised controlled trials.

## What is the difference between a search filter and a query?

Search filters, also called hedges or search blocks, are pre-developed searches for specific topics and designs (2,10). They consist of thesaurus terms (i.e. controlled vocabulary: Medical Subject Headings (MeSH) terms in MEDLINE, EMTREE terms in Embase) and free text terms (i.e. words in titles, abstracts, etc.). These terms are combined using Boolean operators (OR, AND, NOT). For example, the well-known Cochrane Highly Sensitive Search Filter for identifying randomized trials in MEDLINE uses both the term "randomised [tiab]" and "human[mh]", with the search field tag [tiab] denoting a word in the title or abstract and [mh] denoting a MeSH term (6).

The search filter differs from the simple query as their performances are assessed, often through a systematized process (11). Filter performances are based on its capacity to retrieve relevant records in a database (sensitivity) and to discriminate irrelevant records (specificity and precision) (10).

The performances of a search filter are evaluated using a method similar to diagnostic tests (10). The filter is tested on a reference set of records relevant to its topic. Performance
estimates are calculated using a contingency table and should be reported systematically with their confidence intervals. Performances that are the most frequently evaluated are (Table 1):

- **Sensitivity:** the number of relevant records retrieved by the filter divided by the total number of relevant records included in the reference set. The sensitivity estimate depends on the characteristics (scope, journal, readership, *etc*.*) of relevant records included in the reference set, which must be representative of relevant records of the database from which the filter is designed for (MEDLINE, Embase, *etc*.). However, sensitivity remains independent of the prevalence of relevant records in databases.

- **Specificity:** the number of irrelevant records correctly discriminated by the filter divided by the total number of irrelevant records included in the reference set. The specificity estimate depends on the nature (scope, journal, readership) of irrelevant records included in the reference set, which must be representative of irrelevant records found in the database for which the filter is designed (MEDLINE, Embase, *etc*.). As with, specificity is also independent of the prevalence of irrelevant records found in databases.

- **Precision:** the number of relevant records retrieved by the search filter divided by the total number of records (relevant or irrelevant) retrieved by the filter. Precision is equivalent to the positive predictive value for a diagnostic test. The precision estimate depends on the prevalence of relevant records in the reference set, which must be as close as possible to the prevalence of relevant records in the database for which the filter is designed (MEDLINE, Embase, *etc*.).

## Why use a search filter?

The use of search filters with established performance metrics in a search strategy can improve the confidence level assigned to the search strategy. It also allows researchers to
choose the search filter that best suits their needs and resources, depending on whether they are conducting a systematic review, or a non-exhaustive review such as a scoping review or rapid review. For example, a sensitive search filter is designed to promote exhaustiveness and will retrieve more relevant records at the price of retrieving more irrelevant records (or 'noise'). On the other hand, a specific search filter will retrieve fewer records, thus reducing the number of records to be screened, saving time and resources, but increasing the possibility of missing relevant records. Table 2 illustrates the different aims of search filters using examples from Gill et al. (12), which were designed to be either exhaustive or specific in retrieving primary care research publications in Medline via OVID. In addition, search filters are useful to overcome the challenge of retrieving literature that uses different terms to describe the same concept. In the case of primary care literature, a recent study has highlighted the diversity of terms to describe primary care, which also varies by geographical location (13). Therefore, search filters can overcome the challenge of identifying all the synonyms attached to the concept of primary care.

If used in a review, the search filters and overall search strategy should be detailed and available to ensure peer review.

## Where to find search filters?

The first step is to contact the local health librarian or an expert in the field concerned, who can help develop a search filter specifically for the review or know of unpublished filters that can be used. Then several resources should be explored such as database interfaces (i.e. PubMed, Embase.com, OVID, etc.) or search filters repositories. In some cases, search filters have been implemented directly into the database interface - for example, PubMed proposes to help researchers quickly refine their search strategy with validated search filters developed by Haynes et al. called 'Clinical Queries' (14). These search filters can be used directly in a
search tool available on PubMed, and details and performance of the filters are available in the PubMed User Guide (15,16). In addition, the InterTASC Information Specialists Sub-Group (ISSG) has developed a regularly updated repository of all published search filters, accessible through their website, including filters for identifying primary care records (17). The ISSG has also developed a checklist that can help effectively assess a search filter's quality (18). Using the checklist is a time-saving way to select the right filter when many filters are available for a particular topic or design. To date, eight primary care search filters are available in the ISSG repository for use in MEDLINE, the Cochrane Library, and Embase. However, the most recent search filter available was developed more than a decade ago, in 2015 (19), and the terms used to describe primary care are constantly evolving. There is a need to review the performance of these search filters and update them as necessary. The use of AI technology to update them could be considered, as it allows a large number of references to be analysed in a very short time. However, further research is needed to validate an updating process using AI.

## Conclusion

Using search filters in searching primary care literature can only improve how we retrieve research evidence from bibliographic databases. It can also bring us closer to implementation in practice by identifying relevant evidence more effectively and efficiently (2,20). Finally, using tested and validated search filters is aligned with evidence-based practice – making search-related decision based on best available evidence (7). We encourage primary care researchers and clinicians to leverage search filters to make their bibliographic searches and literature reviews more effective and efficient.

## Acknowledgment
Funding statement: This work has not received funding

Data available statement: This work did not produce original data.

Declaration of interest: The authors declare that they have no conflict of interest.
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1. **Figure 1.** Example of a Medline search strategy structure for the research question “What is the efficacy of proton pump inhibitors used in primary care to treat gastroesophageal reflux disease?”

*RCT:* randomized controlled trial, *GERD:* gastroesophageal reflux disease, *PPI:* proton pump inhibitors

2. **Table 1.** Contingency table for the calculation of the performances of a search filter

3. **Table 2.** Examples of sensitive and specific search filters for detecting relevant primary care records in MEDLINE (OvidSP) and their performances as developed by Gill *et al.* (12)