Accuracy and Efficiency of Machine Learning–Assisted Risk-of-Bias Assessments in “Real-World” Systematic Reviews

Abstract

BACKGROUND: Automation is a proposed solution for the increasing difficulty of maintaining up-to-date, high-quality health evidence. Evidence assessing the effectiveness of semiautomated data synthesis, such as risk-of-bias (RoB) assessments, is lacking. OBJECTIVE: To determine whether RobotReviewer-assisted RoB assessments are noninferior in accuracy and efficiency to assessments conducted with human effort only. DESIGN: Two-group, parallel, noninferiority, randomized trial. (Monash Research Office Project 11256). SETTING: Health-focused systematic reviews using Covidence. PARTICIPANTS: Systematic reviewers, who had not previously used RobotReviewer, completing Cochrane RoB assessments between February 2018 and May 2020. INTERVENTION: In the intervention group, reviewers received an RoB form prepopulated by RobotReviewer; in the comparison group, reviewers received a blank form. Studies were assigned in a 1:1 ratio via simple randomization to receive RobotReviewer assistance for either Reviewer 1 or Reviewer 2. Participants were blinded to study allocation before starting work on each RoB form. MEASUREMENTS: Co-primary outcomes were the accuracy of individual reviewer RoB assessments and the person-time required to complete individual assessments. Domain-level RoB accuracy was a secondary outcome. RESULTS: Of the 15 recruited review teams, 7 completed the trial (145 included studies). Integration of RobotReviewer resulted in noninferior overall RoB assessment accuracy (risk difference, -0.014 [95% CI, -0.093 to 0.065]; intervention group: 88.8% accurate assessments; control group: 90.2% accurate assessments). Data were inconclusive for the person-time outcome (RobotReviewer saved 1.40 minutes [CI, -5.20 to 2.41 minutes]). LIMITATION: Variability in user behavior and a limited number of assessable reviews led to an imprecise estimate of the time outcome. CONCLUSION: In health-related systematic reviews, RoB assessments conducted with RobotReviewer assistance are noninferior in accuracy to those conducted without RobotReviewer assistance. PRIMARY FUNDING SOURCE: University College London and Monash University.

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