Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Feb 12, 2025)
Date Submitted: Aug 21, 2023
Open Peer Review Period: Aug 21, 2023 - Oct 16, 2023
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Randomized Clinical Trials can be Accurately Classified in terms of their Methodological Validity by their Representations and Cochrane Reviewers’ Comments
ABSTRACT
Background:
Evidence-based medicine requires evaluating and critically appraising documents. The process needs (semi)automated methods to distinguish valid from invalid randomized clinical trials (RCTs).
Objective:
The present study aimed to test the potential of RCT representations, Cochrane reviewers’ comments, and comment-expanded representations to learn a machine to classify RCTs into different validity categories.
Methods:
To build a test collection, 9,063 RCTs referred to by 132 open-access systematic reviews in the Cochrane database were identified, being tagged as “included” and having PMID. The RCTs’ methodological validity scores were extracted from the Cochrane database and their representative features (i.e., titles, abstracts, and author keywords) from PubMed. To classify the RCTs based on their (in)validity, machine learning was conducted using the kNN algorithm, with 10-fold cross-validation, in the KNIME Data Mining Platform.
Results:
The results showed that the comments and representations can accurately classify RCTs. The classification accuracy values for the comments ranged from 0.758 (for incomplete outcome data) to 0.896 (for potential threats to validity). The accuracy values for the RCT representations ranged from 0.691 (for random sequence generation) to 0.757 (for selective outcome reporting). After expanding the representations by the comments, improvements were observed in the accuracy values, ranging from 0.105 (for blinding of participants, personnel, and outcome assessors) to 0.196 (for random sequence generation).
Conclusions:
The RCTs’ representations and Cochrane reviewers’ comments showed effectiveness in classifying them in their validity categories, with the latter being even more powerful. The comments also enriched the representations and improved their classification accuracy.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.