Profile of betrixaban and its potential in the prevention and treatment of venous thromboembolism
Bibliographic record
Abstract
Venous thromboembolism (VTE), which includes deep vein thrombosis and pulmonary embolism, is a common and potentially preventable cause of morbidity and mortality. Unfractionated heparin, low-molecular-weight heparin, and warfarin have been the cornerstone of VTE prevention and treatment but are being replaced by recently approved non-vitamin K antagonist oral anticoagulants (NOACs): dabigatran, rivaroxaban, apixaban, and edoxaban. The NOACs are at least as effective and as safe as heparins and warfarin for VTE prevention and treatment and are more convenient because they have a low propensity for food and drug interactions and are given in fixed doses without routine coagulation monitoring. The remaining limitations of currently available NOACs include their dependence on renal and hepatic function for clearance, and the lack of an approved antidote. Betrixaban is a new NOAC with distinct pharmacological characteristics: minimal renal clearance, minimal hepatic metabolism, and long half-life. It has undergone successful Phase II studies in orthopedic thromboprophylaxis, and in stroke prevention in atrial fibrillation. Currently, it is being evaluated in a Phase III trial of extended thromboprophylaxis in medical patients (APEX study). In this article, we describe the development of betrixaban, review its pharmacological profile, discuss the results of clinical trials, and examine its potential for VTE prevention and treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".