Rivaroxaban plus aspirin after lower–extremity revascularization
Bibliographic record
Abstract
INTRODUCTION: Patients undergoing revascularization of the lower extremities have unacceptably high rates of major adverse cardiac and limb events despite the routine use of antiplatelet therapy. Optimization of antithrombotic therapy provides an opportunity to reduce this risk. Recent large, randomized trials have demonstrated substantial benefit from the combination of low-dose rivaroxaban and aspirin compared with aspirin alone. Despite this new evidence, uptake remains limited. AREAS COVERED: This review will outline the drug profile of rivaroxaban, summarize the key efficacy and safety data for the combination of low-dose rivaroxaban and aspirin following lower extremity revascularization, and examine barriers to therapy uptake. EXPERT OPINION: Combination of low-dose rivaroxaban and aspirin is the only antithrombotic regimen that has been shown to reduce both cardiac and limb events following peripheral revascularization while maintaining an acceptable bleeding profile. Single and dual antiplatelet therapy have limited randomized evidence for this indication, but are commonly used. An important contributor is the failure of major societal guidelines to incorporate this new evidence. Moving forward, there is an urgent need to update these guidelines. Further evaluation of the efficacy and safety of dual antiplatelet therapy will help to inform optimal antithrombotic therapy after lower extremity revascularization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".