Assessing Risk of Bias in Randomised Clinical Trials Included in Cochrane Reviews: The why is Easy, the how is a Challenge
Bibliographic record
Abstract
Randomised clinical trials are o en inadequately reported and may be inadequately conducted.Any associated biases could impact seriously on the findings and conclusion of a systematic review.Authors of systematic reviews thus need to assess the risk of bias in included randomised clinical trials.In this 20th Anniversary editorial, we look at the evolution of guidance on appraising studies included in Cochrane Reviews.Assessing the methodological 'quality' of included trials was addressed from the earliest days of The Cochrane Collaboration, although the phrase 'risk of bias' came into use later.In 1994 one of the first editions of the Cochrane Collaboration Handbook recommended that reviewers should routinely assess the adequacy of allocation concealment, and that they could consider assessing blinding and attrition, based on a seminal empirical study by Schulz and colleagues.[1]Over the next decade several Cochrane Review Groups developed different recommendations for assessing risk of bias.Of 50 Cochrane Review Groups surveyed in 2007, 41 recommended using specific trial characteristics to assess risk of bias and nine either recommended using a quality scale or made this optional.Most groups suggested assessing the randomisation procedure (including concealment of allocation), blinding, and attrition.[2] Assessing risk of bias in randomised clinical trials included in Cochrane Reviews: the why is easy, the how is a challenge (Editorial) 1
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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 | 0.255 | 0.652 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.019 | 0.029 |
| Insufficient payload (model declined to judge) | 0.004 | 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".