Observed intervention effects for mortality in randomised clinical trials: a methodological study protocol
Bibliographic record
Abstract
INTRODUCTION: It is essential to choose a realistic anticipated intervention effect when calculating a sample size for a randomised clinical trial. Unfortunately, anticipated intervention effects are often inflated, when compared with the 'true' intervention effects. This is documented for mortality in critical care trials. A similar pattern might exist across different medical specialties. This study aims to estimate the range of observed intervention effects for all-cause mortality in trials included in Cochrane Reviews, within each Cochrane Review Group. METHODS AND ANALYSIS: We will include randomised clinical trials assessing all-cause mortality as an outcome. Trials will be identified from Cochrane Reviews published in the Cochrane Database of Systematic Reviews. Cochrane Reviews will be clustered according to the registered Cochrane Review Group (eg, Anaesthesia, Emergency and Critical Care) and the statistical analyses will be conducted for each Cochrane Review Group and overall. The median relative risk and IQR for all-cause mortality and the proportion of trials with a relative all-cause mortality risk within seven different ranges will be reported (relative risk below 0.70, 0.70-0.79, 0.80-0.89, 0.90-1.09, 1.10-1.19, 1.20-1.30 and above 1.30). Subgroup analyses will explore the effects of original design, sample size, risk of bias, disease, intervention type, follow-up length, participating centres, funding type, information size and outcome hierarchy. ETHICS AND DISSEMINATION: Since we will use summary data from trials already approved by relevant ethical committees, this study does not require ethical approval. Regardless of our findings, the results will be published in an international peer-reviewed journal.
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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 | 0.322 | 0.364 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.097 | 0.036 |
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".