Sensitivity and specificity of alternative screening methods for systematic reviews using text mining tools.

J Clin Epidemiol

Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, Canada; Departments of Medicine and of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada; Department of Neurosciences, Université de Montréal, Montreal, Canada; School of Public Health, Université de Montréal, Montreal, Canada. Electronic address:

Published: October 2023

Objectives: To evaluate the impact of text mining (TM) on the sensitivity and specificity of title and abstract screening strategies for systematic reviews (SRs).

Study Design And Setting: Twenty reviewers each evaluated a 500-citation set. We compared five screening methods: conventional double screen (CDS), single screen, double screen with TM, combined double screen and single screen with TM, and single screen with TM. Rayyan, Abstrackr, and SWIFT-Review were used for each TM method. The results of a published SR were used as the reference standard.

Results: The mean sensitivity and specificity achieved by CDS were 97.0% (95% confidence interval [CI]: 94.7, 99.3) and 95.0% (95% CI: 93.0, 97.1). When compared with single screen, CDS provided a greater sensitivity without a decrease in specificity. Rayyan, Abstrackr, and SWIFT-Review identified all relevant studies. Specificity was often higher for TM-assisted methods than that for CDS, although with mean differences of only one-to-two percentage points. For every 500 citations not requiring manual screening, 216 minutes (95% CI: 169, 264) could be saved.

Conclusion: TM-assisted screening methods resulted in similar sensitivity and modestly improved specificity as compared to CDS. The time saved with TM makes this a promising new tool for SR.

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Source
http://dx.doi.org/10.1016/j.jclinepi.2023.07.010DOI Listing

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