Major Bleeding, Mortality, and Efficacy of Fondaparinux in Venous Thromboembolism Prevention Trials
Bibliographic record
Abstract
BACKGROUND: Bleeding is a strong predictor of death in patients hospitalized for arterial thrombosis who are treated with antithrombotic therapy, but the prognostic importance of bleeding in patients receiving antithrombotic prophylaxis for venous thromboembolism is uncertain. METHODS AND RESULTS: Using Cox proportional hazards modeling, we examined the association between major bleeding and death at 30 days using pooled individual patient data from 8 large randomized controlled trials (n=13 085) comparing fondaparinux with control (low-molecular-weight heparin or placebo) for the prophylaxis of venous thromboembolism in hospitalized surgical or medical patients. Patients who developed major bleeding were older, were more likely to be male, had a lower body weight and lower creatinine clearance, and were more likely to be receiving fondaparinux. At 30 days, the risk of death was 7-fold higher among patients with a major bleeding event (8.6% versus 1.7%; adjusted hazard ratio, 6.96; 95% confidence interval, 4.60 to 10.51). There was a consistent pattern of reduced mortality in patients treated with fondaparinux irrespective of whether patients experienced major bleeding (6.8% versus 11.4%; hazard ratio, 0.58; 95% confidence interval, 0.27 to 1.23) or no major bleeding (1.5% versus 1.9%; hazard ratio, 0.77; 95% confidence interval, 0.59 to 1.02; P for heterogeneity=0.47). CONCLUSIONS: Major bleeding in hospitalized surgical and medical patients participating in venous thromboembolism prevention trials is a strong predictor of 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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".