Dual pathway inhibition for atherosclerotic cardiovascular disease: Recent advances
Bibliographic record
Abstract
Atherosclerotic cardiovascular disease (ASCVD), which includes coronary artery disease (CAD), cerebrovascular disease, and peripheral arterial disease (PAD) is associated with significant morbidity, mortality, and healthcare costs. Antiplatelet therapy has long been the mainstay of antithrombotic therapy for the prevention of first-ever and recurrent ASCVD events. More recently, however, randomized trials have demonstrated the benefits and cost-effectiveness of a dual pathway inhibition (DPI) strategy in acute and chronic ASCVD. When used in combination, aspirin and low-dose rivaroxaban work synergistically to inhibit platelet activation and thrombin generation, thereby preventing thrombus formation. Among patients with recent acute coronary syndrome (ACS), those with positive cardiac biomarkers or ST-segment elevation myocardial infarction, or a history of heart failure derive the greatest absolute benefits. Among patients with chronic ASCVD, those with involvement of two or more vascular beds, heart failure, chronic kidney disease, or diabetes derive the greatest absolute benefits. Additional trials are underway to assess the impact of DPI therapy in other populations of interest, including patients with ACS at high risk of left ventricular thrombus formation, intracranial atherosclerotic disease with recent transient ischemic attack or stroke, peripheral arterial disease with limiting claudication or post lower extremity revascularization, and advanced chronic kidney disease with ASCVD or risk factors for ASCVD. Further work is required to assess the possible added benefit of combining rivaroxaban with clopidogrel or ticagrelor instead of aspirin.
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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 | 0.003 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".