Prevention of thromboembolism in the patient with acute coronary syndrome and atrial fibrillation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Atrial fibrillation in patients with acute coronary syndrome (ACS) is associated with a high thromboembolic event rate. Combined oral anticoagulant (OAC) and antiplatelet therapy (APT) are often used to reduce thromboembolic risk, recurrent coronary ischemic events, and stent thrombosis, despite the high bleeding risk. This review is timely with the recent introduction of novel OACs (NOACs), more potent antiplatelet agents, and second-generation coronary stents with a lower risk of late stent thrombosis, and considers strategies and new opportunities to reduce both thrombotic events and bleeding. RECENT FINDINGS: The benefits of NOACs in patients with atrial fibrillation have been shown in recent studies. New evidence indicates that single rather than dual APT may be adequate when an OAC is used in a patient with a recent coronary stent. Limited evidence suggests a NOAC is preferable to warfarin when additional APT is also required. SUMMARY: The implications of the new findings are to indicate strategies for more effective antithrombotic therapy, while minimizing the risk of major bleeding in patients with ACS and atrial fibrillation. However, additional research studies are required to further optimize treatment strategies in this high-risk population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".