Novel Antiplatelet Agent Use for Acute Coronary Syndrome in the Emergency Department: A Review
Bibliographic record
Abstract
Background. Acute Coronary Syndrome (ACS) is a clinical condition encompassing ST Segment Elevation Myocardial Infarction (STEMI), Non-ST Segment Elevation Myocardial Infarction (NSTEMI), and Unstable Angina (UA) and is characterized by ruptured coronary plaque, ischemic stress, and/or myocardial injury. Emergency department (ED) physicians are on the front lines of ACS management. The role of new antiplatelet agents ticagrelor and prasugrel in acute ED management of ACS has not yet been defined. Objective. To critically review clinical trials using ticagrelor and prasugrel in the treatment of ACS and inform practitioners of their potential utility in treating ACS in the ED. Results. Trials on the efficacy of ticagrelor and prasugrel achieve statistical significance in decreasing composite endpoints in select patient populations. Conclusion. The use of ticagrelor and prasugrel as first line ED treatment of ACS is not well established. Current evidence supports the use of several agents with the final decision based on treatment protocols conjointly developed between cardiology and emergency medicine (EM). Further clinical trials involving head-to-head trials or comparisons of drug-based strategies are required to show superiority in reducing cardiac endpoints with regard to ED initiation of treatment.
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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 | 0.004 | 0.004 |
| 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) | 0.004 | 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".