Title, abstract, and keyword searching resulted in poor recovery of articles in systematic reviews of epidemiologic practice

Bas B.L. Penning de Vries*, Maarten van Smeden, Frits R. Rosendaal, Rolf H.H. Groenwold

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

17 Citations (Scopus)

Abstract

Objective: Article full texts are often inaccessible via the standard search engines of biomedical literature, such as PubMed and Embase, which are commonly used for systematic reviews. Excluding the full-text bodies from a literature search may result in a small or selective subset of articles being included in the review because of the limited information that is available in only title, abstract, and keywords. This article describes a comparison of search strategies based on a systematic literature review of all articles published in 5 top-ranked epidemiology journals between 2000 and 2017. Study Design and Setting: Based on a text-mining approach, we studied how nine different methodological topics were mentioned across text fields (title, abstract, keywords, and text body). The following methodological topics were studied: propensity score methods, inverse probability weighting, marginal structural modeling, multiple imputation, Kaplan-Meier estimation, number needed to treat, measurement error, randomized controlled trial, and latent class analysis. Results: In total, 31,641 Hypertext Markup Language (HTML) files were downloaded from the journals’ websites. For all methodological topics and journals, at most 50% of articles with a mention of a topic in the text body also mentioned the topic in the title, abstract, or keywords. For several topics, a gradual decrease over calendar time was observed of reporting in the title, abstract, or keywords. Conclusion: Literature searches based on title, abstract, and keywords alone may not be sufficiently sensitive for studies of epidemiological research practice. This study also illustrates the potential value of full-text literature searches, provided there is accessibility of full-text bodies for literature searches.

Original languageEnglish
Pages (from-to)55-61
Number of pages7
JournalJournal of Clinical Epidemiology
Volume121
DOIs
Publication statusPublished - May 2020
Externally publishedYes

Keywords

  • Bibliometrics
  • Epidemiological methods
  • Statistical methods
  • Systematic literature review
  • Text mining

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