Early and Late Effects of Clopidogrel in Patients With Acute Coronary Syndromes
Bibliographic record
Abstract
BACKGROUND: The risk of ischemic events is high, both early and late after acute coronary syndromes (ACS). We examine the benefits and risks associated with the use of adding clopidogrel to aspirin within the first 30 days and later (31 days to 12 months) in 12 562 patients with ACS. METHODS AND RESULTS: A total of 12 562 ACS patients were randomized to receive clopidogrel (300 mg initially followed by 75 mg/d) or placebo for 3 to 12 months. The proportion of patients experiencing cardiovascular death, myocardial infarction, or strokes (primary outcome) at 30 days was 5.4% in the placebo group and 4.3% in the active group (relative risk 0.79, 95% CI 0.67 to 0.92). Beyond 30 days, the corresponding rates were 6.3% versus 5.2% (relative risk 0.82, 95% CI 0.70 to 0.95). There was no significant excess in life-threatening bleeds in each period (0.97% versus 1.28%, relative risk 1.32, 95% CI 0.95 to 1.84 for 0 to 30 days; 0.83% versus 0.91%, relative risk 1.09, 95% CI 0.75 to 1.59 for 31 days to 12 months). Further subdivision of the early data indicates benefits within 24 hours with consistently lower rates of the primary outcome in combination with refractory or severe ischemia. CONCLUSIONS: Clopidogrel reduces the risk of ischemic vascular events, with the benefits emerging within 24 hours of initiation of treatment and continuing throughout the 12 months (mean 9 months) of the study.
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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 | 0.002 | 0.004 |
| 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) | 0.001 | 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".