Dual versus single antiplatelet therapy in patients with non-cardioembolic acute ischemic stroke and baseline MRI
Bibliographic record
Abstract
INTRODUCTION: Dual antiplatelet therapy (DAPT) is superior to single antiplatelet therapy (SAPT) for secondary prevention after minor, non-cardioembolic stroke. We aimed to assess whether DAPT efficacy is modified by large artery atherosclerotic (LAA) etiology, and DAPT safety by stroke size on MRI. PATIENTS AND METHODS: Post hoc analysis of the Phase 2 PACIFIC-STROKE randomized clinical trial, which enrolled patients with non-cardioembolic stroke, all with baseline MRI and compared the Factor XIa inhibitor asundexian with placebo on a background of DAPT or SAPT. We compared patients treated with DAPT versus SAPT. The efficacy endpoint was the rate of recurrent ischemic stroke, the safety endpoint was major or clinically relevant non-major bleeding during follow-up. RESULTS: 1590 patients were included, median NIHSS was 2 (interquartile range [IQR] 1-4), 40% received DAPT. Median follow-up was 11.5 months. The efficacy endpoint occurred in 4.4% and 4.8% in the DAPT group and SAPT group, respectively, with the strongest numerical benefit of DAPT over SAPT among patients with NIHSS ⩽ 3 not treated by intravenous thrombolysis. LAA index stroke etiology did not modify DAPT treatment effect. The safety endpoint occurred more often in the DAPT than in the SAPT group (4.6% vs 2.7%), with the numerically lowest risk among patients with NIHSS ⩽ 3 not treated by intravenous thrombolysis. Stroke size did not modify the effect of DAPT on the safety endpoint. DISCUSSION AND CONCLUSION: We found no evidence of major treatment effect heterogeneity with DAPT compared with SAPT in patients with and without LAA or by stroke size on MR-DWI.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".