Stroke Outcomes With Vorapaxar Versus Placebo in Patients With Acute Coronary Syndromes: Insights From the TRACER Trial
Bibliographic record
Abstract
Background Vorapaxar, a protease-activated receptor-1 antagonist, is approved for secondary prevention of cardiovascular events but is associated with increased intracranial hemorrhage. Methods and Results TRACER (Thrombin Receptor Antagonist for Clinical Event Reduction in Acute Coronary Syndrome) was a trial of vorapaxar versus placebo among patients with acute coronary syndrome. Strokes were adjudicated by a central events committee. Of 12 944 patients, 199 (1.5%) had ≥1 stroke during the study period (median follow-up, 477 days). Four patients had a single stroke of unknown type; 195 patients had ≥1 stroke classified as hemorrhagic or nonhemorrhagic (165 nonhemorrhagic, 28 hemorrhagic, and 2 both). Strokes occurred in 96 of 6473 patients (1.5%) assigned vorapaxar and 103 of 6471 patients (1.6%) assigned placebo. Kaplan-Meier incidence of stroke for vorapaxar versus placebo was higher for hemorrhagic stroke (0.45% versus 0.14% [hazard ratio, 2.74; 95% confidence interval, 1.22-6.15]), lower but not significantly different for nonhemorrhagic stroke (1.53% versus 1.98% at 2 years [hazard ratio, 0.79; 95% confidence interval, 0.58-1.07]), and similar for stroke overall (1.93% versus 2.13% at 2 years [hazard ratio, 0.94; 95% confidence interval, 0.71-1.24]). Conclusions Stroke occurred in <2% of patients. Vorapaxar-assigned patients had increased hemorrhagic stroke but a nonsignificant trend toward lower nonhemorrhagic stroke. Overall stroke frequency was similar with vorapaxar versus placebo.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".