Literature DB >> 29289761

Technology-assisted risk of bias assessment in systematic reviews: a prospective cross-sectional evaluation of the RobotReviewer machine learning tool.

Allison Gates1, Ben Vandermeer1, Lisa Hartling2.   

Abstract

OBJECTIVES: To evaluate the reliability of RobotReviewer's risk of bias judgments. STUDY DESIGN AND
SETTING: In this prospective cross-sectional evaluation, we used RobotReviewer to assess risk of bias among 1,180 trials. We computed reliability with human reviewers using Cohen's kappa coefficient and calculated sensitivity and specificity. We investigated differences in reliability by risk of bias domain, topic, and outcome type using the chi-square test in meta-analysis.
RESULTS: Reliability (95% CI) was moderate for random sequence generation (0.48 [0.43, 0.53]), allocation concealment (0.45 [0.40, 0.51]), and blinding of participants and personnel (0.42 [0.36, 0.47]); fair for overall risk of bias (0.34 [0.25, 0.44]); and slight for blinding of outcome assessors (0.10 [0.06, 0.14]), incomplete outcome data (0.14 [0.08, 0.19]), and selective reporting (0.02 [-0.02, 0.05]). Reliability for blinding of participants and personnel (P < 0.001), blinding of outcome assessors (P = 0.005), selective reporting (P < 0.001), and overall risk of bias (P < 0.001) differed by topic. Sensitivity and specificity (95% CI) ranged from 0.20 (0.18, 0.23) to 0.76 (0.72, 0.80) and from 0.61 (0.56, 0.65) to 0.95 (0.93, 0.96), respectively.
CONCLUSION: Risk of bias appraisal is subjective. Compared with reliability between author groups, RobotReviewer's reliability with human reviewers was similar for most domains and better for allocation concealment, blinding of participants and personnel, and overall risk of bias.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Automation; Bias; Clinical trials; Evaluation; Evidence-based medicine; Machine learning

Mesh:

Year:  2017        PMID: 29289761     DOI: 10.1016/j.jclinepi.2017.12.015

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  3 in total

1.  Machine learning to help researchers evaluate biases in clinical trials: a prospective, randomized user study.

Authors:  Frank Soboczenski; Thomas A Trikalinos; Joël Kuiper; Randolph G Bias; Byron C Wallace; Iain J Marshall
Journal:  BMC Med Inform Decis Mak       Date:  2019-05-08       Impact factor: 2.796

2.  Post-stroke fatigue: a scoping review.

Authors:  Ghazaleh Aali; Avril Drummond; Roshan das Nair; Farhad Shokraneh
Journal:  F1000Res       Date:  2020-04-07

3.  Care technologies to prevent and control hemorrhage in the third stage of labor: a systematic review.

Authors:  Rita de Cássia Teixeira Rangel; Maria de Lourdes de Souza; Cheila Maria Lins Bentes; Anna Carolina Raduenz Huf de Souza; Maria Neto da Cruz Leitão; Fiona Ann Lynn
Journal:  Rev Lat Am Enfermagem       Date:  2019-08-19
  3 in total

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