Novel Oral Factor Xa and Thrombin Inhibitors in the Management of Thromboembolism
Bibliographic record
Abstract
The last decade has seen the evaluation of several new oral anticoagulants that directly target thrombin or activated factor X (FXa). All demonstrate a rapid onset of action, a low potential for food and drug interactions, and a predictable anticoagulant effect that obviates the need for routine coagulation monitoring. Those agents at the most advanced stages of clinical development are a direct thrombin inhibitor, dabigatran, and direct FXa inhibitors, rivaroxaban and apixaban. Dabigatran and rivaroxaban are approved in more than 70 countries for prevention of venous thromboembolism in patients undergoing elective hip or knee arthroplasty, and apixaban is being considered for approval by regulatory agencies for this indication. Dabigatran was shown in a large phase III trial to be more effective and safer than warfarin for the prevention of stroke or systemic embolism in patients with atrial fibrillation and has recently been approved for this indication. Edoxaban, an oral FXa inhibitor, is also being evaluated in phase III clinical trials. This review summarizes the pharmacology, clinical trial results, and future role of the new oral anticoagulants in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".