Colchicine for stroke prevention in patients with coronary artery disease: a systematic review and meta‐analysis
Bibliographic record
Abstract
Background and purpose Although clinical trials suggest that colchicine may reduce the risk of vascular events in patients with a history of coronary artery disease, its effect on the prevention of cerebrovascular events still remains unclear. Methods A systematic review and meta‐analysis was performed of all available randomized controlled trials (RCTs) reporting on incident strokes during the follow‐up of patients with a history of cardiovascular disease randomized to colchicine treatment or control (placebo or usual care). Results Four RCTs were identified, including a total of 5553 patients (mean age 61 years, 81% males), with a follow‐up ranging from 1 to 36 months. Colchicine treatment was associated with a significantly lower risk of incident stroke during follow‐up compared to control (risk ratio 0.31, 95% confidence interval 0.13–0.71), without heterogeneity across included studies (I2 = 0%). Based on the pooled incident stroke rate of control groups (0.9%) in the included RCTs, it was estimated that administration of low‐dose colchicine to 161 patients with coronary artery disease would prevent one stroke during a follow‐up of 23 months. Conclusion Colchicine treatment decreases stroke risk in patients with a history of coronary artery disease. The effect of colchicine in secondary stroke prevention is currently being evaluated in an ongoing RCT.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".