Original ArticleMachine learning reduced workload with minimal risk of missing studies: development and evaluation of a randomized controlled trial classifier for Cochrane Reviews
Keywords
Cited by (0)
Funding: This work received funding from Cochrane via Project Transform; Australian National Health & Medical Research Council (Partnership Project grant APP1114605); U.S. National Library of Medicine (Award 2R01-LM012086-05); I.J.M. was supported by a Medical Research Council (UK) fellowship (MR/N015185/1). A portion of James Thomas's time was supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care North Thames at Barts Health NHS Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
Ethics approval and consent to participate: Not applicable: study does not involve human subjects.
Conflict of interest: The authors declare that they have no competing interests.
Authors’ contributions: J.T., A.N.S., S.M., and I.J.M. designed the study. J.T. and I.J.M. built the classifiers and calibration models. A.N.S. and S.M. worked on evaluation data sets. C.M. provided overall Cochrane direction and governance. I.S. and J.E. provided methodological input throughout. All authors read and approved the final article.
Consent for publication: Not applicable: no participant data presented.
Availability of data and materials: Hyperlinks to source code repositories are supplied in the text. Cochrane's CENTRAL database is referenced and is available at https://www.cochranelibrary.com/central. All Cochrane Crowd labels are open and available at http://crowd.cochrane.org/DownloadData.php.