Improving clinical outcomes by reducing bleeding in patients with non-ST-elevation acute coronary syndromes
Bibliographic record
Abstract
AIMS: Bleeding in patients with coronary artery disease has been linked with adverse outcomes. We examined the incidence and outcomes after bleeding in 20 078 patients with acute coronary syndromes (ACS) enrolled in the OASIS-5 trial who were treated with fondaparinux or the low-molecular weight heparin, enoxaparin. METHODS AND RESULTS: Nine hundred and ninety (4.9%) patients developed major bleeding and 423 (2.1%) developed minor bleeding. Fondaparinux compared with enoxaparin reduced fatal bleeding [0.07 vs. 0.22%, relative risk (RR) 0.30, 95% CI: 0.13-0.71], non-fatal major bleeding (2.2 vs. 4.2%, RR 0.52, 95% CI: 0.44-0.61), minor bleeding (1.1 vs. 3.2%, RR 0.34, 95% CI: 0.27-0.42), and need for transfusion (1.8 vs. 3.1%, RR 0.56, 95% CI: 0.47-0.61) during the first 9 days. One of every six deaths during the first 30 days occurred in patients who experienced bleeding. Cox proportional hazards model revealed that major bleeding was associated with about a four-fold increased hazard of death, myocardial infarction, or stroke during the first 30 days and about a three-fold increased hazard during 180 days of follow up. CONCLUSION: Bleeding in patients with ACS is a powerful determinant of fatal and non-fatal outcomes. Reducing the risk of bleeding using a safer anticoagulant strategy during the first 9 days is associated with substantial reductions in morbidity and mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".