Low-dose colchicine for stroke prevention: A systematic overview of systematic reviews and meta-analyses
Bibliographic record
Abstract
BACKGROUND: Stroke incidence remains a significant concern despite optimized prevention strategies. Colchicine shows potential for improving stroke prevention globally. AIMS: To summarize efficacy and safety estimates from systematic reviews and meta-analyses (SRMAs) of randomized controlled trials (RCTs) comparing colchicine to usual care or placebo for stroke prevention. METHODS: We conducted an overview of SRMAs according to the Preferred Reporting Items for Overviews of Reviews guidelines through a systematic search in Pubmed, Embase, and the Cochrane Library. Statistical analysis was performed using RevMan Web. Heterogeneity was assessed with I² statistics. RESULTS: Thirty-two studies were included. Colchicine significantly reduced stroke recurrence (RR 0.46; 95 % CI 0.41-0.52; p < 0.0001; I² = 0 %; OR 0.44, 95 % CI 0.36-0.55; p < 0.0001; I² = 0 %) but increased gastrointestinal adverse events (RR 1.54, 95 % CI 1.33-1.79; p < 0.0001; I² = 63 %; OR 1.60, 95 % CI 1.08-2.38; p = 0.0007; I² = 82 %). Most SRMAs (93.75 %) showed reduced stroke incidence (RR 0.26-0.54), while 65.22 % reported increased gastrointestinal events (RR 1.05-2.66). No significant differences were observed in mortality, infection or cancer rates. Overall quality was appraised as high in 28.12 %, moderate in 6.25 %, low in 40.06 %, and critically low in 25 % of SRMAs. Data were primarily derived from seven RCTs with low risk of bias. CONCLUSIONS: Moderate-quality evidence supports colchicine's benefits and reasonable safety for preventing stroke among high-risk populations. However, stroke was not the primary endpoint in analyzed studies. RCTs directly assessing colchicine for stroke prevention are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.021 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".