The Combination of Oral Anticoagulant and Antiplatelet Therapies: Stay One Step Ahead
Bibliographic record
Abstract
Antithrombotic drugs, which include antiplatelets and anticoagulants, are effective in prevention and treatment of many cardiovascular disorders such as acute coronary syndromes, stroke, and venous thromboembolism and are among the drugs most commonly prescribed worldwide. The advent of direct oral anticoagulants, which are safer alternatives to vitamin K antagonists and do not require laboratory monitoring, has revolutionized the treatment of nonvalvular atrial fibrillation and venous thromboembolism. The combination of oral anticoagulant and antiplatelet therapy is required in many conditions of great clinical impact such as the coexistence of atrial fibrillation and coronary artery disease, with indication to percutaneous coronary intervention. However, strategies that combine anticoagulant and antiplatelet therapies lead to a significant increase in bleeding rates and it is crucial to find the right combination in the single patient in order to optimize the ischemic and bleeding risk. The aim of this review is to explore the evidence and controversies regarding the optimal combination of anticoagulant and antiplatelet therapy through the consideration of past dogmas and new perspectives from recent clinical trials and to propose a tailored therapeutic approach, according to specific clinical scenarios and individual patient characteristics. In particular, we separately explored the clinical settings of stable and acute coronary syndromes and percutaneous revascularization in patients with atrial fibrillation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".