Benefit of Glycoprotein IIb/IIIa Inhibition in Patients With Acute Coronary Syndromes and Troponin T–Positive Status
Bibliographic record
Abstract
BACKGROUND: Troponin T (TnT) is valuable for short- and long-term risk stratification of patients with acute coronary syndromes (ACS). It also may predict which ACS patients will benefit from glycoprotein (GP) IIb/IIIa blockade. METHODS AND RESULTS: We prospectively studied 1160 patients with non-ST-segment elevation ACS randomized in PARAGON-B to receive lamifiban, an intravenous GP IIb/IIIa antagonist, or placebo. TnT levels were obtained before study treatment began and 24 to 72 hours later; assays were performed by a blinded core laboratory. At baseline, 40.2% of patients were TnT-positive (>/=0.1 ng/mL); these patients were older and more often male or smokers. Patients positive at baseline had a significantly higher rate of the primary end point (composite of death, myocardial [re]infarction, or severe recurrent ischemia at 30 days; odds ratio, 1.5; 95% CI, 1.1 to 2.1) than those who were TnT-negative. Lamifiban was associated with significant reduction in the primary end point (from 19.4% to 11.0%, P=0.01) among TnT-positive patients but not among TnT-negative patients (11.2% for placebo versus 10.8% for lamifiban, P=0.86; P=0.08 for test of interaction between TnT status and treatment assignment). This pattern held for the end points of death alone and death or myocardial (re)infarction at 30 days. Peak TnT level at 48 hours did not differ with lamifiban treatment. CONCLUSIONS: TnT predicts poor short-term outcomes in non-ST-segment elevation ACS. Treatment benefit with lamifiban is limited almost exclusively to TnT-positive patients, reducing 30-day adverse outcomes to a rate nearly identical to that of negative 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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".