Advances in oral anticoagulation treatment: the safety and efficacy of rivaroxaban in the prevention and treatment of thromboembolism
Bibliographic record
Abstract
Arterial and venous thromboembolic diseases are a clinical and economic burden worldwide. In addition to traditional agents such as vitamin K antagonists and heparins, newer oral agents - such as the factor Xa inhibitors rivaroxaban, apixaban, and edoxaban, and the direct thrombin inhibitor dabigatran - have been shown to be effective across several indications. Rivaroxaban has been shown to have predictable pharmacokinetic and pharmacodynamic properties, including a rapid onset of action. In addition, there is no requirement for routine coagulation monitoring; and no dose adjustment is necessary for age alone, sex, or body weight. Rivaroxaban has successfully met primary efficacy and safety endpoints in large, randomized phase III trials across several indications, including: prevention of venous thromboembolism in orthopedic patients undergoing elective hip or knee replacement surgery; treatment of deep vein thrombosis and secondary prevention of deep vein thrombosis and pulmonary embolism; stroke prevention in patients with atrial fibrillation; and secondary prevention of acute coronary syndrome. Rivaroxaban and the other newer oral anticoagulants are likely to improve outcomes in the prevention and treatment of thromboembolic events, and will offer patients and physicians alternative treatment options.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".