Publication

Novel citation-based search method for scientific literature: application to meta-analyses

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Last modified
  • 02/20/2025
Type of Material
Authors
    Anna Janssens, Emory UniversityM. Gwinn, Vrije Universiteit Amsterdam
Language
  • English
Date
  • 2015-12
Publisher
  • BioMed Central
Publication Version
Copyright Statement
  • © 2015 Janssens and Gwinn.
License
Final Published Version (URL)
Title of Journal or Parent Work
ISSN
  • 1471-2288
Volume
  • 15
Issue
  • 1
Start Page
  • 84
Grant/Funding Information
  • A.C.J.W. Janssens acknowledges financial support by the European Research Council (ERC) Consolidator Grant GENOMICMEDICINE.
Supplemental Material (URL)
Abstract
  • Background Finding eligible studies for meta-analysis and systematic reviews relies on keyword-based searching as the gold standard, despite its inefficiency. Searching based on direct citations is not sufficiently comprehensive. We propose a novel strategy that ranks articles on their degree of co-citation with one or more “known” articles before reviewing their eligibility. Method In two independent studies, we aimed to reproduce the results of literature searches for sets of published meta-analyses (n = 10 and n = 42). For each meta-analysis, we extracted co-citations for the randomly selected ‘known’ articles from the Web of Science database, counted their frequencies and screened all articles with a score above a selection threshold. In the second study, we extended the method by retrieving direct citations for all selected articles. Results In the first study, we retrieved 82 % of the studies included in the meta-analyses while screening only 11 % as many articles as were screened for the original publications. Articles that we missed were published in non-English languages, published before 1975, published very recently, or available only as conference abstracts. In the second study, we retrieved 79 % of included studies while screening half the original number of articles. Conclusions Citation searching appears to be an efficient and reasonably accurate method for finding articles similar to one or more articles of interest for meta-analysis and reviews.
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Research Categories
  • Health Sciences, General
  • Information Science

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