2259Ticagrelor in patients wtih symptomatic peripheral artery disease and prior coronary artery disease
Bibliographic record
Abstract
Background: The optimal long-term antithrombotic regimen in patients with symptomatic peripheral artery disease (PAD) and a history of coronary artery disease (CAD) is uncertain. Methods: The EUCLID trial randomized 13,885 patients with symptomatic PAD to antithrombotic monotherapy with ticagrelor 90 mg twice daily or clopidogrel 75 mg daily (NCT01732822). Patients were enrolled based on an abnormal ankle-brachial index ≤0.80 or prior lower extremity revascularization. We identified those enrolled with prior CAD (prior myocardial infarction [MI], percutaneous coronary intervention [PCI], or coronary artery bypass graft surgery [CABG]). The primary efficacy endpoint was a composite of cardiovascular death, MI, or ischemic stroke. The primary safety endpoint was TIMI major bleeding. Median follow-up was ≈30 months. Results: Of 4032 patients with PAD and prior CAD, 63% had a prior MI, 49% prior PCI, and 38% prior CABG. After adjustment for baseline characteristics, PAD patients with prior CAD had significantly higher rates of the composite primary endpoint (15.3% vs. 8.9%, HR 1.50, 95% CI 1.13–1.99, p=0.005), but no increase in acute limb ischemia (ALI) (1.6% vs. 1.7%, HR 1.28, 95% CI 0.57–2.85, p=0.55) or major bleeding (1.8% vs. 1.5%, HR 1.10, 95% CI 0.49–2.48, p=0.81), as compared with PAD patients without CAD. Among patients with PAD and prior CAD, there were no differences between ticagrelor vs. clopidogrel for the primary efficacy endpoint (15.4% vs. 15.3%; HR 1.02, 95% CI 0.87–1.19; p=0.84), ALI (1.6% vs. 1.5%; HR 1.03, 95% CI 0.63–1.69; p=0.89), or major bleeding (1.7% vs. 1.8%; HR 1.06, 95% CI 0.66–1.69; p=0.81). There was a significant interaction between prior stent placement and study treatment (p=0.03) with a numeric trend towards a reduction in the primary efficacy endpoint with ticagrelor (13.8% vs. 16.8%, HR 0.82, 95% CI 0.65–1.03, p=0.09).
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".