An evaluation of DistillerSR's machine learning-based prioritization tool for title/abstract screening - impact on reviewer-relevant outcomes.

Journal: BMC medical research methodology
Published Date: Oct 15, 2020

Abstract

BACKGROUND: Systematic reviews often require substantial resources, partially due to the large number of records identified during searching. Although artificial intelligence may not be ready to fully replace human reviewers, it may accelerate and reduce the screening burden. Using DistillerSR (May 2020 release), we evaluated the performance of the prioritization simulation tool to determine the reduction in screening burden and time savings.

Authors

  • C Hamel
    Clinical Epidemiology Program, Ottawa Hospital Research Institute, 501 Smyth Road, Box 201b, Ottawa, Ontario, K1H 8L6, Canada. cahamel@ohri.ca.
  • S E Kelly
    Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, Ottawa, Ontario, Canada.
  • K Thavorn
    Clinical Epidemiology Program, Ottawa Hospital Research Institute, 501 Smyth Road, Box 201b, Ottawa, Ontario, K1H 8L6, Canada.
  • D B Rice
    Clinical Epidemiology Program, Ottawa Hospital Research Institute, 501 Smyth Road, Box 201b, Ottawa, Ontario, K1H 8L6, Canada.
  • G A Wells
    Clinical Epidemiology Program, Ottawa Hospital Research Institute, 501 Smyth Road, Box 201b, Ottawa, Ontario, K1H 8L6, Canada.
  • B Hutton
    Clinical Epidemiology Program, Ottawa Hospital Research Institute, 501 Smyth Road, Box 201b, Ottawa, Ontario, K1H 8L6, Canada.

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