Oral anticoagulation with factor Xa and thrombin inhibitors: on the threshold of change
Bibliographic record
Abstract
PURPOSE OF REVIEW: Since the discovery of vitamin K antagonists in the early 1940s, there has been little change in anticoagulation until, in the 1990s, widespread use of low-molecular-weight heparin. Within the next few years, the treatment of arterial and venous thromboembolism is again poised to undergo a major change with the introduction of new oral anticoagulants that are likely to fulfill many of the unmet needs of current warfarin therapy. New drug development has focused on inhibiting specific coagulation factors, with those targeting thrombin and factor Xa being most advanced in development. RECENT FINDINGS: Several landmark studies are now available on the direct thrombin inhibitor, dabigatran etexilate, and the two factor Xa inhibitors, rivaroxaban and apixaban. Recently, dabigatran etexilate received European approval for venous thromboembolism prevention following orthopedic surgery. Rivaroxaban is currently also approved in Europe and Canada for venous thromboembolism prevention in orthopedic surgery, with US Food and Drug Administration approval expected in 2009. SUMMARY: New oral anticoagulant approval may provide safer and easier venous thromboembolism prevention and treatment than warfarin. As we stand on this threshold, this article reflects on anticoagulation breakthroughs, summarizes recent studies, and discusses potential drawbacks.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".