Dabigatran as anticoagulant therapy for atrial fibrillation. Which patients should receive it, which patients may not need it, and other practical aspects of patient management
Bibliographic record
Abstract
In the past decade, antithrombotic therapy research has focused on the development of new oral anticoagulant drugs to replace vitamin K antagonists for stroke prevention in patients with chronic atrial fibrillation, for preventing cardiovascular complications of acute coronary syndromes, and for the prevention and treatment of venous thromboembolism. The most anticipated studies relate to the use of new oral anticoagulants to replace vitamin K antagonists for the prevention of stroke in patients with atrial fibrillation. This review will focus on dabigatran, the first non-vitamin K anticoagulant approved for this clinical indication, and will assess the RE-LY trial (Randomized Evaluation of Long Term Anticoagulant Therapy) findings according to the level of anticoagulation control in warfarin-treated patients. The objectives of this review are: 1) to provide an overview of dabigatran, highlighting clinically relevant properties; 2) to provide a commentary on the study by Wallentin et al. within the context of how the quality of anticoagulation control affects warfarin efficacy and safety; and 3) to consider which patients with chronic atrial fibrillation should receive and which may not need to receive dabigatran.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".