Antithrombotic therapy for stroke prevention in atrial fibrillation and mechanical heart valves
Bibliographic record
Abstract
Cardioembolic strokes account for one-sixth of all strokes and are an important potentially preventable cause of morbidity and mortality. Vitamin K antagonists (e.g., warfarin) are effective for the prevention of cardioembolic stroke in patients with atrial fibrillation (AF) and in those with mechanical heart valves but because of their inherent limitations are underutilized and often suboptimally managed. Antiplatelet therapies have been the only alternatives to warfarin for stroke prevention in AF but although they are safer and more convenient they are much less efficacious. The advent of new oral anticoagulant drugs offers the potential to reduce the burden of cardioembolic stroke by providing access to effective, safe, and more convenient therapies. New oral anticoagulants have begun to replace warfarin for stroke prevention in some patients with AF, based on the favorable results of recently completed phase III randomized controlled trials, and provide for the first time an alternative to antiplatelet therapy for patients deemed unsuitable for warfarin. The promise of the new oral anticoagulants in patients with mechanical heart valves is currently being tested in a phase II trial. If efficacy and safety are demonstrated, the new oral anticoagulants will provide an alternative to warfarin for patients with mechanical heart valves and may also lead to increased use of mechanical valves for patients who would not have received them in the past because of the requirement for long term warfarin therapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".