Efficacy of Antiplatelet Therapy in Secondary Prevention Following Lacunar Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Lacunar stroke accounts for ≈25% of ischemic stroke, but optimal antiplatelet regimen to prevent stroke recurrence remains unclear. We aimed to evaluate the efficacy of antiplatelet agents in secondary stroke prevention after a lacunar stroke. METHODS: We searched MEDLINE, Embase, and the Cochrane library for randomized controlled trials that reported risk of recurrent stroke or death with antiplatelet therapy in patients with lacunar stroke. We used random effects meta-analysis and evaluated heterogeneity with I(2). RESULTS: We included 17 trials with 42,234 participants (mean age 64.4 years, 65% male) and follow up ranging from 4 weeks to 3.5 years. Compared with placebo, any single antiplatelet agent was associated with a significant reduction in recurrence of any stroke (risk ratio [RR] 0.77, 0.62-0.97, 2 studies) and ischemic stroke (RR 0.48, 0.30-0.78, 2 studies), but not for the composite outcome of any stroke, myocardial infarction, or death (RR 0.89, 0.75-1.05, 2 studies). When other antiplatelet agents (ticlodipine, cilostazol, and dipyridamole) were compared with aspirin, there was no consistent reduction in stroke recurrence (RR 0.91, 0.75-1.10, 3 studies). Dual antiplatelet therapy did not confer clear benefit over monotherapy (any stroke RR 0.83, 0.68-1.00, 3 studies; ischemic stroke RR 0.80, 0.62-1.02, 3 studies; composite outcome RR 0.90, 0.80-1.02, 3 studies). CONCLUSIONS: Our results suggest that any of the single antiplatelet agents compared with placebo in the included trials is adequate for secondary stroke prevention after lacunar stroke. Dual antiplatelet therapy should not be used for long-term stroke prevention in this stroke subtype.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".