Complementary effects of thienopyridine pretreatment and platelet glycoprotein IIb/IIIa integrin blockade with eptifibatide in coronary stent intervention; Results from the ESPRIT trial
Bibliographic record
Abstract
OBJECTIVES: This analysis sought to investigate the complementary effect of thienopyridine pretreatment and platelet glycoprotein (GP) IIb/IIIa integrin blockade in coronary stent intervention. BACKGROUND: Definitive evidence supporting combined antiplatelet therapy consisting of thienopyridine pretreatment and GP IIb/IIIa receptor blockade in patients undergoing percutaneous coronary intervention (PCI) with stent implantation is limited. METHODS: We retrospectively analyzed clinical outcomes by thienopyridine use in the 2,040 patients randomized to eptifibatide or placebo who underwent PCI in the ESPRIT trial. RESULTS: A total of 901 patients received a loading dose of thienopyridine before PCI (group 1), 123 received thienopyridine pretreatment without a loading dose (group 2), and 1,016 were not treated with thienopyridine before PCI (group 3). The composite incidence of death or myocardial infarction at 30 days was significantly lower in group 1 than in groups 2 and 3 combined (OR, 0.71 [95%CI, 0.52-0.99]; P = 0.0417). A similar trend was seen for the composite of death, myocardial infarction, or urgent target vessel revascularization (unadjusted OR, 0.77 [0.57-1.05]; P = 0.1025). After adjusting for baseline characteristics, these differences were no longer significant. No interactions were identified with eptifibatide assignment for any of the group comparisons. CONCLUSIONS: Pretreatment with a loading dose of thienopyridine lowers the rate of ischemic complications regardless of treatment with a GP IIb/IIIa inhibitor. Conversely, the efficacy of eptifibatide is maintained whether or not a loading dose of a thienopyridine is administered. Optimal outcomes are achieved in patients receiving thienopyridine pretreatment along with platelet GP IIb/IIIa inhibitor therapy.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".