Efficacy of Colchicine for Secondary Prevention of Stroke: A Systematic Review and Meta-Analysis of Randomized Control Trials
Bibliographic record
Abstract
Colchicine, a long-established anti-inflammatory medication, has emerged as a potential therapeutic agent for secondary prevention of stroke. This systematic review and meta-analysis aimed to evaluate the efficacy and safety of colchicine in preventing secondary stroke by comprehensively synthesizing available evidence. A systematic literature search was conducted across multiple electronic databases from inception to November 15, 2024, using comprehensive search strategies. Randomized controlled trials involving colchicine administration for stroke prevention were included. Two independent reviewers screened studies, extracted data, and assessed methodological quality using the Cochrane Risk of Bias tool. Meta-analysis was performed using Review Manager software, with risk ratios calculated for stroke incidence and all-cause mortality. The analysis encompassed seven studies involving 23,303 participants. The meta-analysis revealed a borderline significant 24% relative risk reduction in stroke incidence (risk ratio 0.76, 95% confidence interval 0.57-1.00, p = 0.05). Moderate heterogeneity was observed among studies (I² = 50%). Importantly, no significant difference was found in all-cause mortality between colchicine and control groups (risk ratio 1.03, 95% confidence interval 0.91-1.17, p = 0.66). While the findings suggest potential benefits of colchicine in stroke prevention, the results warrant cautious interpretation. The study emphasizes the need for larger, well-designed randomized controlled trials to definitively establish colchicine's role in comprehensive stroke prevention strategies.
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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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.043 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".