Dabigatran and Warfarin in Vitamin K Antagonist–Naive and –Experienced Cohorts With Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: The comparison of anticoagulants dabigatran and warfarin might be most equitable in vitamin K antagonist (VKA)-naive patients. METHODS AND RESULTS: Warfarin and 2 doses of dabigatran-110 mg BID (D110) and 150 mg BID (D150)-were compared in a balanced population of VKA-naive (≤62 days of lifetime VKA exposure, with 33% never prescribed a VKA) and VKA-experienced patients with atrial fibrillation (n=18 113). For VKA-naive and -experienced patients assigned warfarin, the time in therapeutic range (international normalized ratio 2.0 to 3.0) was 62% and 67%, respectively, and 61% and 66% for those never and ever prescribed a VKA. In VKA-naive patients, stroke and systemic embolism rates were 1.57%, 1.07%, and 1.69% per year for D110, D150, and warfarin, respectively. D110 was similar to warfarin (P=0.65); D150 was superior (P=0.005). Major bleeding rates were 3.11%, 3.34%, and 3.57% per year, respectively. D110 and D150 were similar to warfarin (P=0.19 and P=0.55). Intracranial bleeding rates were 0.19%, 0.33%, and 0.73% per year, respectively. D110 and D150 were lower than warfarin (P<0.001 and P=0.005). In VKA-experienced patients, stroke and systemic embolism rates were 1.51%, 1.15%, and 1.74% per year for D110, D150, and warfarin, respectively. D110 was similar to warfarin (P=0.32); D150 was superior (P=0.007). Major bleeding rates were 2.66%, 3.30%, and 3.57% per year, respectively. D110 was lower than warfarin (P=0.003); D150 was similar (P=0.41). Intracranial bleeding rates were 0.26%, 0.32%, and 0.79% per year, respectively. D110 and D150 were lower than warfarin (P<0.001 for both). Results were similar for patients never on a VKA. CONCLUSIONS: Previous VKA exposure does not influence the benefits of dabigatran at either dose compared with warfarin. CLINICAL TRIAL REGISTRATION: http://www.clinicaltrials.gov. Unique identifier: NCT00262600.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".