Rivaroxaban for venous thromboembolism prevention after major orthopedic surgery: translating trial data into routine clinical practice
Bibliographic record
Abstract
An established standard of care for the prevention of venous thromboembolism after major orthopedic surgery has been subcutaneous low-molecular-weight heparin. The non-vitamin K antagonist oral anticoagulant rivaroxaban has demonstrated superior efficacy and similar safety to all tested regimens of enoxaparin in large Phase III clinical studies of venous thromboembolism prevention after elective hip and knee arthroplasty. Despite regulatory approval of rivaroxaban for this indication, concerns remain among physicians regarding its optimal and effective use in routine clinical practice. Real-life studies, such as XAMOS and ORTHO-TEP, are providing physicians with more information on the routine use of rivaroxaban for venous thromboembolism prevention after orthopedic surgery, helping to establish its safety and effectiveness in everyday clinical care. Among the most important issues are the risk of bleeding complications, wound healing, timing of first dose, impact of type of anesthesia on thromboprophylaxis effectiveness, patient comorbidities and comedication use, periprocedural management, associated costs, and clinical outcomes in trauma-related fractures. Many of these issues are difficult to study in randomized, double-blind, Phase III trials, and can be assessed more readily using real-life data. In particular, real-life or noninterventional studies lack many of the strict inclusion and exclusion criteria associated with Phase III trials and involve unselected patients who often present with significant comorbidities or comedication use.
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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 | 0.240 | 0.440 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| 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".