Secondary Stroke Prevention With Ximelagatran Versus Warfarin in Patients With Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Patients with nonvalvular atrial fibrillation and prior stroke or transient ischemic attack (TIA) are at high risk for recurrent stroke. We investigated whether ximelagatran was noninferior to warfarin in patients with prior stroke or TIA. METHODS: We analyzed pooled data from the SPORTIF III and V trials in patients with prior stroke/TIA. The primary outcome was the composite annual rate of both ischemic and hemorrhagic strokes and systemic embolic events. Secondary analyses considered ischemic and hemorrhagic strokes separately, bleeding, and nonrandomized, concomitant therapy with aspirin < or =100 mg/d. RESULTS: Patients from SPORTIF III (n=3407) and SPORTIF V (n=3922) trials were categorized by prior stroke/TIA (21%) versus no prior stroke/TIA (79%) and by treatment group (ximelagatran vs warfarin). The primary event rate in patients with prior stroke/TIA was 2.83%/y with ximelagatran and 3.27%/y with warfarin (absolute difference, -0.44%; 95% CI, -1.88 to1.01; P=0.625). In those without prior stroke/TIA, the primary event rate was 1.31%/y with ximelagatran and 1.26%/y with warfarin (P=NS). Ischemic strokes outnumbered cerebral hemorrhages with both warfarin (31 of 36) and ximelagatran (30 of 32) treatment (difference between treatments was not significant). Combining aspirin with either anticoagulant was associated with higher rates of major bleeding (1.5%/y with warfarin and 4.95%/y with warfarin plus aspirin, P=0.004; 2.35%/y with ximelagatran and 5.09%/y with ximelagatran plus aspirin, P=0.046) but not lower rates of primary events. CONCLUSIONS: Ximelagatran was at least as effective as well-controlled warfarin for the secondary prevention of stroke. The nonrandomized, concomitant treatment with aspirin and anticoagulation was associated with increased bleeding without evidence of a reduction in primary outcome events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".