Meta-regression of randomized control trials with antithrombotics: weak correlation between net clinical benefit and all cause-mortality
Bibliographic record
Abstract
Abstract This study aimed to explore the validity of the use of the net clinical benefit (NCB), i.e. the sum of major bleeding and thrombotic events, as a potential surrogate for all-cause mortality in clinical trials assessing antithrombotics. Published randomized controlled trials testing anticoagulants in the prevention or treatment of venous thromboembolism (VTE) and non-valvular atrial fibrillation (NVAF) were systematically reviewed. The validity of NCB as a surrogate endpoint was estimated by calculating the strength of correlation of determination (R2) and its 95% confidence interval (CI) between the relative risks of NCB and all-cause mortality. Amongst the 125 trials retrieved, the highest R2trial values were estimated for NVAF (R2trial = 0.41, 95% CI [0.03; 0.48]), and acute VTE (R2trial = 0.30, 95% CI [0.04; 0.84]). Conversely, the NCB did not correlate with all-cause mortality in prevention studies with medical (R2trial = 0.12, 95% CI [0.00; 0.36]), surgical (R2trial = 0.05, 95% CI [0.00; 0.23]), and cancer patients (R2trial = 0.006, 95% CI [0.00; 1.00]). A weak correlation between NCB and all cause-mortality was found in NVAF and acute VTE, whereas no correlation was observed in clinical situations where the mortality rate was low. Consequently, NCB should not be considered a surrogate outcome for all cause-mortality in anticoagulation trials.
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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 | 0.144 | 0.239 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.055 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".