Does eptifibatide confer a greater benefit to patients with unstable angina than with non-ST segment elevation myocardial infarction?. Insights from the PURSUIT Trial
Bibliographic record
Abstract
AIMS: To evaluate the differential effects of eptifibatide therapy on unstable angina vs non-ST elevation myocardial infarction at enrollment, since the separate impact on these two major diagnostic subsets of acute coronary syndrome patients has not been fully investigated. METHODS AND RESULTS: We examined the 9461 patients in the PURSUIT trial (conducted between 1995 and 1997) to compare the effects of eptifibatide on unstable angina and myocardial infarction. The study showed greater and more consistent effects of eptifibatide therapy on unstable angina than non-ST elevation myocardial infarction in reducing 30-day death/(re)infarction (from the unadjusted rate of 13.0% to 11.2%, P=0.059 for unstable angina; and 18.9% to 17.9%, P=0.387 for myocardial infarction), especially among patients who underwent early percutaneous coronary intervention (odds ratios=0.49 and 0.86, 95% confidence intervals=0.30-0.80 and 0.53-1.42, respectively, for unstable angina and myocardial infarction). The only subgroup for whom the benefit of eptifibatide was not evident was female myocardial infarction patients who did not undergo early percutaneous coronary intervention. CONCLUSIONS: These data suggest that eptifibatide benefited unstable angina patients more than myocardial infarction patients, especially among those who underwent early percutaneous coronary intervention, and support its use as concomitant therapy with early percutaneous coronary intervention especially in female myocardial infarction patients.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".