Antithrombotic Therapy in Patients With Atrial Fibrillation Undergoing Percutaneous Coronary Intervention
Bibliographic record
Abstract
The optimal antithrombotic treatment regimen for patients with atrial fibrillation undergoing percutaneous coronary intervention with stent implantation is an emerging clinical problem. Currently, there is limited evidenced-based data on the optimal antithrombotic treatment regimen, including antiplatelet and anticoagulant therapies, for these high-risk patients with practice guidelines, thus, providing limited recommendations. Over the past years, expert consensus documents have provided guidance to clinicians on how to manage patients with atrial fibrillation undergoing percutaneous coronary intervention. Given the recent advancements in the field, the current document provides an updated opinion of selected North American experts from the United States and Canada on the treatment of patients with atrial fibrillation undergoing percutaneous coronary intervention. In particular, this document provides the current views on (1) embolic/stroke risk, (2) ischemic/thrombotic cardiac risk, and (3) bleeding risk, which are pivotal for discerning the choice of antithrombotic therapy. In addition, we describe the recent advances in pharmacology, stent designs, and clinical trials relevant to the field. Ultimately, we provide expert consensus-derived recommendations, using a pragmatic approach, on the management of patients with atrial fibrillation undergoing percutaneous coronary intervention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.000 |
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".