Which are the most sensitive search filters to identify randomized controlled trials in MEDLINE?

Authors

  • Julie Glanville York Health Economics Consortium, University of York, York
  • Eleanor Kotas York Health Economics Consortium, University of York, York
  • Robin Featherstone Editorial and Methods Department, Cochrane, London
  • Gordon Dooley Metaxis, Oxford

DOI:

https://doi.org/10.5195/jmla.2020.912

Keywords:

Literature Search Effectiveness, Literature Searching, Randomized Controlled Trials, Search Filters

Abstract

Objective: The Cochrane Handbook of Systematic Reviews contains search filters to find randomized controlled trials (RCTs) in Ovid MEDLINE: one maximizing sensitivity and another balancing sensitivity and precision. These filters were originally published in 1994 and were adapted and updated in 2008. To determine the performance of these filters, the authors tested them and thirty-six other MEDLINE filters against a large new gold standard set of relevant records.

Methods: We identified a gold standard set of RCT reports published in 2016 from the Cochrane CENTRAL database of controlled clinical trials. We retrieved the records in Ovid MEDLINE and combined these with each RCT filter. We calculated their sensitivity, relative precision, and f-scores.

Results: The gold standard comprised 27,617 records. MEDLINE searches were run on July 16, 2019. The most sensitive RCT filter was Duggan et al. (sensitivity=0.99). The Cochrane sensitivity-maximizing RCT filter had a sensitivity of 0.96 but was more precise than Duggan et al. (0.14 compared to 0.04 for Duggan). The most precise RCT filters had 0.97 relative precision and 0.83 sensitivity.

Conclusions: The Cochrane Ovid MEDLINE sensitivity-maximizing RCT filter can continue to be used by Cochrane reviewers and to populate CENTRAL, as it has very high sensitivity and a slightly better precision relative to more sensitive filters. The results of this study, which used a very large gold standard to compare the performance of all known RCT filters, allows searchers to make better informed decisions about which filters to use for their work.

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Published

2020-10-01

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Section

Original Investigation