Tolerating bad health research (part 2): still as many bad trials, but more good ones too
Bibliographic record
Abstract
BACKGROUND: We previously published a study examining the risk of bias of a random selection of Cochrane systematic reviews. The purpose of our current study is to reassess the risk of bias of a cohort of Cochrane reviewed trials to see if our reassessment differs from the original Cochrane assessment and to determine whether the funder, having methodological support, or involving a statistician affected the risk of bias. METHODS: We extracted data from 140 of 159 included trials from three countries, the UK, Canada, and Ireland, in our original cohort. The 19 remaining trials were excluded for a variety of reasons. We recorded the number of participants in the trial, the funder, if a statistician was involved in the trial, if there was any methodological support from a trials unit or clinical research facility, the sponsor, and whether the sponsor was involved in the design or conduct of the trial. The risk of bias of the 140 trials was re-assessed using the same tool as that used by the Cochrane authors. RESULTS: Our judgement of overall high risk of bias was broadly consistent with the original Cochrane authors. The proportion of high risk of bias trials remained more or less where it was at 55%, but the proportion of low risk of bias trials increased from 9 to 16%. The proportion of unclear risk of bias trials changed accordingly. Compared to the original assessments, we judged more studies to be low risk of bias across all domains. The greatest variation was in the two blinding categories (participants and personnel; outcome assessor) and 'other bias'. CONCLUSIONS: More than half of trials in our UK, Canada, and Ireland cohort were at high risk of bias highlighting significant challenges in ensuring the integrity and reliability of research findings. Addressing bias in clinical trials is essential to uphold the credibility of scientific research and to ensure that healthcare interventions are based on sound evidence, ultimately improving patient outcomes.
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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 | 0.675 | 0.870 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.021 | 0.025 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".