Search filters

Last revised: 
2017-04-05

What are search filters?

Search filters (sometimes called hedges) are collections of search terms designed to retrieve selections of records from a bibliographic database (1). Search filters may be designed to retrieve records of research using a specific study design (e.g. randomised controlled trial) or topic (kidney disease) or some other feature of the research question (age of study’s participants). They are usually combined with the results of a subject search using the AND operator.

Why you would use a search filter?

When included in a database search strategy, a robust search filter can significantly reduce the number of records that researchers may need to sift and recent research has shown that this is a key use of search filters (2). Search filters are not available, however, for all study types or all databases or all database interfaces.

Key features

Filters are typically designed for one purpose, which may be to maximise sensitivity (or recall) or to maximise precision (and reduce the number of irrelevant records that need to be assessed for relevance). Sensitivity is the proportion of relevant records retrieved by the filter and is the most frequently reported performance measure (3). Precision is the proportion of relevant records in the retrieved records and is also frequently reported (3). Specificity is the proportion of irrelevant records successfully not retrieved. Filters are database and interface specific. Performance measures can be difficult to interpret and alternative graphical approaches to presenting performance information may assist with making decisions about which filter to select (3).

Where can you find search filters?

Search filters of interest to researchers producing technology assessments are incorporated into some of the MEDLINE interfaces. For example, they are labelled as Clinical Queries in PubMed (4). Often searchers ‘translate’ filters or adapt them to run on different interfaces (2). Translations and adaptations should be undertaken carefully since different interfaces function in different ways, and different databases may have different indexing languages.

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

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

Critical appraisal of filters

When published, the methods used to compile search filters should be clearly described by the authors. It is also valuable to have access to critical assessments of filters in practice. Search filter development methods have developed over time to become more objective and rigorous (1, 4).  The quality of search filters can be appraised using critical appraisal tools (5, 6) 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. The value of a search filter can decrease over time as new terms are added to a database thesaurus.

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. 

A systematic review of the performance of a large number of diagnostic test accuracy (DTA) filters has provided recommendations that search filters should not be used as the only method for searching for DTA studies for systematic reviews and technology appraisals (7). The review concludes that the filters risk missing relevant studies and do not offer benefits in terms of enhanced precision.

A comparison study (8) 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.

More recently a study (9) demonstrated that a search filter with adequate precision and sensitivity was not yet available to identify studies of epidemiology in the MEDLINE  database

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 can be created by using relative recall (10) or by handsearching.

A recent case study (11) describes how such 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: how to generate the initial set of records; the sample size required for filter development; use of an annotation tool and annotation guidelines; and the calibration process to measure inter-annotator agreement.
 

Reference List