Colchicine for secondary prevention of vascular events: a meta-analysis of trials
Bibliographic record
Abstract
BACKGROUND AND AIMS: Randomized trials of colchicine in secondary prevention of atherosclerotic cardiovascular disease have shown mixed results. METHODS: A systematic review and study-level meta-analysis of randomized controlled trials was performed comparing colchicine vs no colchicine in a secondary-prevention atherosclerotic cardiovascular disease population. A fixed-effect inverse variance model was applied using the intention-to-treat population from the included trials. The primary outcome was the composite of cardiovascular death, myocardial infarction, or stroke. RESULTS: Nine trials, including 30 659 patients (colchicine 15 255, no colchicine 15 404) with known coronary artery disease or stroke, were included. Compared with no colchicine, patients randomized to colchicine had a relative risk (RR) of 0.88 [95% confidence interval (CI) 0.81-0.95, P = .002] for the primary composite outcome, including a RR of 0.94 for cardiovascular death (95% CI 0.78-1.13, P = .5), a RR of 0.84 for myocardial infarction (95% CI 0.73-0.97, P = .016), and a RR of 0.90 for stroke (95% CI 0.80-1.02, P = .09). Colchicine was associated with a RR of 1.35 for hospitalization for gastrointestinal events (95% CI 1.10-1.66, P = .004) with no increase in hospitalization for pneumonia, newly diagnosed cancers, or non-cardiovascular death. CONCLUSIONS: In patients with prior coronary disease or stroke, colchicine reduced the composite of cardiovascular death, myocardial infarction, or stroke by 12%.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.046 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".