Effect of Ximelagatran and Warfarin on Stroke Subtypes in Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The most common stroke subtype among atrial fibrillation (AF) patients not receiving anticoagulants is cardioembolic. In the SPORTIF III and V trials, the oral direct thrombin inhibitor ximelagatran was as effective as warfarin in reducing the risk of stroke in patients with nonvalvular AF. We assessed any differential effect of warfarin versus ximelagatran on the risk and outcome of cardioembolic and noncardioembolic stroke. METHODS: 7329 patients with AF and > or = 1 risk factors for stroke were randomized to treatment with warfarin (target international normalized ratio 2.0--3.0) or fixed-dose ximelagatran. Strokes were classified into specific subtypes. Therapeutic effect of warfarin and ximelagatran, adverse events, and stroke outcomes were assessed according to stroke subtype. RESULTS: The annual stroke rate was low for both cardioembolic (ximelagatran, 0.39%; warfarin, 0.47%) and noncardioembolic stroke (ximelagatran, 0.57%; warfarin, 0.37%). In ischemic strokes, 33.9% (ximelagatran) and 34.3% (warfarin) had strokes of presumed cardioembolic origin. When fatal stroke, disabling stroke, myocardial infarction, and death from any cause were combined as poor outcome, patients with cardioembolic strokes had the highest rate of poor outcome (40%) but this was non- significant. CONCLUSIONS: In SPORTIF III and V the efficacy of warfarin and ximelagatran were similar for prevention of cardioembolic and noncardioembolic strokes. Overall outcome tended to be worse following cardioembolic stroke. Ximelagatran has been withdrawn from the market due to hepatic side effects, but similar compounds are presently being studied.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".