Colchicine for prevention of vascular inflammation in Non-CardioEmbolic stroke (CONVINCE) – study protocol for a randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: Inflammation contributes to unstable atherosclerotic plaque and stroke. In randomised trials in patients with coronary disease, canukinumab (an interleukin-1B antagonist) and colchicine (a tubulin inhibitor with pleiotropic anti-inflammatory effects) reduced recurrent vascular events.Hypothesis: Anti-inflammatory therapy with low-dose colchicine plus usual care will reduce recurrent vascular events in patients with non-severe, non-cardioembolic stroke and TIA compared with usual care alone. DESIGN: CONVINCE is a multi-centre international (in 17 countries) Prospective, Randomised Open-label, Blinded-Endpoint assessment (PROBE) controlled Phase 3 clinical trial in 3154 participants. The intervention is colchicine 0.5 mg/day and usual care versus usual care alone (antiplatelet, lipid-lowering, antihypertensive treatment, lifestyle advice). Included patients are at least 40 years, with non-severe ischaemic stroke (modified Rankin score ≤3) or high-risk TIA (ABCD2 > 3, or positive DWI, or cranio-cervical artery stenosis) within 72 hours-28 days of randomisation, with qualifying stroke/TIA most likely caused by large artery stenosis, lacunar disease, or cryptogenic embolism. Exclusions are stroke/TIA caused by cardio-embolism or other defined cause (e.g. dissection), contra-indication to colchicine (including potential drug interactions), or incapacity for participation in a clinical trial. The anticipated median follow-up will be 36 months. The primary analysis will be by intention-to-treat. OUTCOME: The primary outcome is time to first recurrent ischaemic stroke, myocardial infarction, cardiac arrest, or hospitalisation with unstable angina (non-fatal or fatal). SUMMARY: CONVINCE will provide high-quality randomised data on the efficacy and safety of anti-inflammatory therapy with colchicine for secondary prevention after stroke. SCHEDULE: First-patient first-visit was December 2016. Recruitment to complete in 2021, follow-up to complete in 2023.
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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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.008 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.085 | 0.017 |
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".