The Role of Direct Oral Anticoagulants in Patients With Coronary Artery Disease
Bibliographic record
Abstract
Despite contemporary management, patients with coronary artery disease (CAD) remain at high risk for thrombotic events. Several randomized controlled trials have evaluated the use of direct oral anticoagulants (DOACs) in patients with CAD, including in the setting of acute coronary syndrome (ACS) and stable CAD, and in patients with concomitant atrial fibrillation. Trials of apixaban and dabigatran in patients with ACS demonstrate no benefit with an increased risk of bleeding. Conversely, rivaroxaban at a reduced dose of 2.5 mg twice daily reduced thrombotic events and all-cause mortality when added to dual antiplatelet therapy in patients with ACS. Similarly, the addition of low-dose rivaroxaban to acetylsalicylic acid reduced the risk of thrombotic events in patients with stable CAD. However, the addition of a DOAC to antiplatelet therapy increased the risk of major bleeding. In patients with atrial fibrillation undergoing percutaneous coronary intervention, dual-pathway or low-dose triple therapy regimens including dabigatran or rivaroxaban reduced bleeding risk compared to traditional warfarin-based triple therapy, although it remains unclear whether these regimens preserve antithrombotic efficacy. DOAC-based antithrombotic regimens prove useful in patients with CAD in various settings; however, careful selection of patients and regimens per trial protocols are critical to achieving net benefit.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".