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

Jimmy Li1, Joudy Kabouji2, Sarah Bouhadoun3

  • 1Neurology Division, Centre Hospitalier de l'Université de Sherbrooke (CHUS), Sherbrooke, Canada; Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, Canada.

Journal of Clinical Epidemiology
|July 28, 2023
PubMed
Summary

Text mining (TM) improves systematic review (SR) screening by maintaining high sensitivity and modestly increasing specificity. TM-assisted methods save significant time, making them a valuable tool for SRs.

Keywords:
AbstrackrArtificial intelligenceDiagnostic studyKnowledge synthesisMachine learningRayyanSWIFT-ReviewSensitivitySpecificity

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Area of Science:

  • Information Science
  • Medical Informatics
  • Evidence-Based Medicine

Background:

  • Systematic reviews (SRs) rely on comprehensive literature searches.
  • Title and abstract screening are critical but time-consuming steps in SRs.
  • Text mining (TM) offers potential for automating parts of the screening process.

Purpose of the Study:

  • To evaluate the impact of text mining (TM) on the sensitivity and specificity of title and abstract screening for systematic reviews (SRs).
  • To compare the efficiency of TM-assisted screening methods against conventional double screening (CDS).

Main Methods:

  • Twenty reviewers screened 500 citations using five methods: CDS, single screen, double screen with TM, combined double screen and single screen with TM, and single screen with TM.
  • Three TM tools (Rayyan, Abstrackr, SWIFT-Review) were utilized.
  • A published SR served as the reference standard.

Main Results:

  • Conventional double screening (CDS) achieved 97.0% sensitivity and 95.0% specificity.
  • TM-assisted methods demonstrated similar sensitivity and modestly improved specificity (1-2 percentage points higher) compared to CDS.
  • TM-assisted screening saved an average of 216 minutes per 500 citations not requiring manual screening.

Conclusions:

  • Text mining (TM) assisted screening methods are comparable to CDS in sensitivity and offer modest specificity improvements.
  • The significant time savings make TM a promising tool for enhancing systematic review efficiency.