Publication year
2019Source
Health Libraries Review, 36, 4, (2019), pp. 318-340ISSN
Publication type
Article / Letter to editor

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Organization
Health Evidence
Human Genetics
Otorhinolaryngology
IQ Healthcare
Journal title
Health Libraries Review
Volume
vol. 36
Issue
iss. 4
Page start
p. 318
Page end
p. 340
Subject
Radboudumc 13: Stress-related disorders DCMN: Donders Center for Medical Neuroscience; Radboudumc 17: Women's cancers RIHS: Radboud Institute for Health Sciences; Radboudumc 18: Healthcare improvement science RIHS: Radboud Institute for Health Sciences; Radboudumc 5: Inflammatory diseases RIHS: Radboud Institute for Health SciencesAbstract
BACKGROUND: Treatment effects differ across patients. To guide selection of treatments for patients, it is essential to acknowledge these differences and identify moderators or predictors. Our aim was to generate optimal search strategies (commonly known as filters) for PubMed to retrieve papers identifying moderators and predictors of treatment effects. METHODS: Six journals were hand-searched for articles on moderators or predictors. Selected articles were randomly allocated to a development and validation set. Search terms were extracted from the development set and tested for their performance. Search filters were created from combinations of these terms and tested in the validation set. RESULTS: Of 4407 articles, 198 were considered to be relevant. The most sensitive filter in the development set '("Epidemiologic Methods" [MeSH] OR assign* OR control*[tiab] OR trial*[tiab]) AND therapy*[sh]' yielded in the validation set a sensitivity of 89% [88%-90%] and a specificity of 80% [79%-82%]. CONCLUSIONS: The search filters created in this study can help to efficiently retrieve evidence on moderators and predictors of treatment effect. Testing of the filters in multiple domains should reveal robustness across disciplines. These filters can facilitate the retrieval of evidence on moderators and predictors of treatment effects, helping the implementation of stratified or personalised health care.
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- Faculty of Medical Sciences [86219]
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