The evolving role of dabigatran etexilate in clinical practice
Bibliographic record
Abstract
INTRODUCTION: Stroke and venous thromboembolism (VTE) affect millions of patients. The vitamin K antagonist, warfarin, has been the main oral anticoagulant used to treat these conditions despite many limitations associated with its use. Recently, multiple novel oral anticoagulants have been approved and are reshaping how patients with atrial fibrillation (AF) at risk of stroke and patients with VTE are treated. The direct thrombin inhibitor, dabigatran etexilate , is among these novel agents that have been developed to overcome limitations with warfarin. AREAS COVERED: In this article, authors describe the pharmacokinetic and pharmacodynamic properties of dabigatran etexilate and summarize the clinical evidence and controversy surrounding its use in the US, Canada and Europe. EXPERT OPINION: Dabigatran has demonstrated similar efficacy and safety to enoxaparin for VTE prevention in patients undergoing hip and knee arthroplasty, and to warfarin for the treatment of VTE. Dabigatran (110 mg) is noninferior and dabigatran (150 mg) is superior to warfarin for stroke prevention in patients with nonvalvular AF, with a lower rate of intracranial hemorrhage reported at both doses. Apixaban, rivaroxaban and edoxaban provide alternate anticoagulant options to dabigatran. While there are many similarities, there are also significant differences to consider in agent selection based on patient-specific characteristics.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".