A search filter is a search strategy also known as a search hedge, that has been developed to define certain criteria for your search. Many databases feature a built-in set of search filters that are commonly used to limit search results by age group, publication type, study type, and more. The Centre for Reviews and Dissemination has a list of published filters along with information on appraisal of search filters, links to articles that have evaluated filters, etc. When selecting a filter, be sure to look at the date of development as changes to database terms and structure can affect the performance of the search filter.
When conducting a systematic review, however, there is a possibility that these filters may exclude relevant studies. For this reason, search experts and institutions have developed their own search filters, and many are available online for public use. This guide will show some examples from various databases.
A well-designed search filter will very deliberately return extremely comprehensive results. We want high sensitivity-- if there is any possibility an article is describing some kind of whatever it is that filter is trying to find, that citation should be returned in the search. Ideally, a search filter should approach 100% sensitivity.
To use the search filter, run your search as you normally do in the database of your choice. After you are done combining your terms, find the filter based on the database you are searching and the research methodology you want. Copy the search filter and paste it into the search text box. Run the search, then use and to combine it with your search.
There are several factors to consider when choosing a search filter.
The search filters in this guide have been tested by:
1. Comparing against a list of known items. The search filter should find all items in the list.
2. Comparing against results from other filters. The search filter should find all of the relevant items found by other filters and, we hope, reduce the number of non-relevant items
3. Taking the results of a subject search, applying the filter, then reviewing what was left after the subject filter terms were removed. In the PubMed search example, the results of #1 NOT #3 would be reviewed to determine if maybe a possible systematic review was missed. If so, the citation and MeSH terms would be scanned to figure out WHY is was missed. Once the appropriate terms were determined, they would be added to the search filter.
4. Comparing a possible new filter against the old filter. We want new terms to improve sensitivity; we do not want them to drastically reduce specificity! In other words, more true positives should be found without greatly increasing the number of false positives. If that happens, then the search is modified to find ways to improve the latter without impacting the former.
5. Reviewing new vocabulary and adding as appropriate. For example, there are many types of systematic reviews: scoping reviews, umbrella reviews (also called "systematic review of systematic reviews"), and so forth. A search filter should include those terms as well as the more general terms, systematic review and systematic literature review.