Impact of peer review on reports of randomised trials published in open peer review journals: retrospective before and after study
Bibliographic record
Abstract
OBJECTIVE: To investigate the effectiveness of open peer review as a mechanism to improve the reporting of randomised trials published in biomedical journals. DESIGN: Retrospective before and after study. SETTING: BioMed Central series medical journals. SAMPLE: 93 primary reports of randomised trials published in BMC-series medical journals in 2012. MAIN OUTCOME MEASURES: Changes to the reporting of methodological aspects of randomised trials in manuscripts after peer review, based on the CONSORT checklist, corresponding peer reviewer reports, the type of changes requested, and the extent to which authors adhered to these requests. RESULTS: Of the 93 trial reports, 38% (n=35) did not describe the method of random sequence generation, 54% (n=50) concealment of allocation sequence, 50% (n=46) whether the study was blinded, 34% (n=32) the sample size calculation, 35% (n=33) specification of primary and secondary outcomes, 55% (n=51) results for the primary outcome, and 90% (n=84) details of the trial protocol. The number of changes between manuscript versions was relatively small; most involved adding new information or altering existing information. Most changes requested by peer reviewers had a positive impact on the reporting of the final manuscript--for example, adding or clarifying randomisation and blinding (n=27), sample size (n=15), primary and secondary outcomes (n=16), results for primary or secondary outcomes (n=14), and toning down conclusions to reflect the results (n=27). Some changes requested by peer reviewers, however, had a negative impact, such as adding additional unplanned analyses (n=15). CONCLUSION: Peer reviewers fail to detect important deficiencies in reporting of the methods and results of randomised trials. The number of these changes requested by peer reviewers was relatively small. Although most had a positive impact, some were inappropriate and could have a negative impact on reporting in the final publication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.723 | 0.948 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".