Search

This column explains ways to optimize the PubMed search features: Computed Author Sort, PubMed Identifier, PubMed Phrase Index, and proximity search. Two case studies show how to find every citation in PubMed, and how to retrieve comprehensive citations to systematic reviews. The article concludes with why PubMed ignores some search terms. [ABSTRACT FROM AUTHOR]

Objective: There is a need for additional comprehensive and validated filters to find relevant references more efficiently in the growing body of research on immigrant populations. Our goal was to create reliable search filters that direct librarians and researchers to pertinent studies indexed in PubMed about health topics specific to immigrant populations. Methods: We applied a systematic and multi-step process that combined information from expert input, authoritative sources, automation, and manual review of sources. We established a focused scope and eligibility criteria, which we used to create the development and validation sets. We formed a term ranking system that resulted in the creation of two filters: an immigrant-specific and an immigrant-sensitive search filter. Results: When tested against the validation set, the specific filter sensitivity was 88.09%, specificity 97.26%, precision 97.88%, and the NNR 1.02. The sensitive filter sensitivity was 97.76%when tested against the development set. The sensitive filter had a sensitivity of 97.14%, specificity of 82.05%, precision of 88.59%, accuracy of 90.94%, and NNR [See Table 1] of 1.13 when tested against the validation set. Conclusion: We accomplished our goal of developing PubMed search filters to help researchers retrieve studies about immigrants. The specific and sensitive PubMed search filters give information professionals and researchers options to maximize the specificity and precision or increase the sensitivity of their search for relevant studies in PubMed. Both search filters generated strong performance measurements and can be used as-is, to capture a subset of immigrantrelated literature, or adapted and revised to fit the unique research needs of specific project teams (e.g. remove UScentric language, add location-specific terminology, or expand the search strategy to include terms for the topic/s being investigated in the immigrant population identified by the filter). There is also a potential for teams to employ the search filter development process described here for their own topics and use. [ABSTRACT FROM AUTHOR]

Objective: Locating systematic reviews is essential for clinicians and researchers when creating or updating reviews and for decision-making in health care. This study aimed to develop a search filter for retrieving systematic reviews that improves upon the performance of the PubMed systematic review search filter. Methods: Search terms were identified from abstracts of reviews published in Cochrane Database of Systematic Reviews and the titles of articles indexed as systematic reviews in PubMed. Both the precision of the candidate terms and the number of systematic reviews retrieved from PubMed were evaluated after excluding the subset of articles retrieved by the PubMed systematic review filter. Terms that achieved a precision greater than 70% and relevant publication types indexed with MeSH terms were included in the filter search strategy. Results: The search strategy used in our filter added specific terms not included in PubMed's systematic review filter and achieved a 61.3% increase in the number of retrieved articles that are potential systematic reviews. Moreover, it achieved an average precision that is likely greater than 80%. Conclusions: The developed search filter will enable users to identify more systematic reviews from PubMed than the PubMed systematic review filter with high precision. [ABSTRACT FROM AUTHOR]