Association of Bleeding Severity With Mortality in Extended Thromboprophylaxis of Medically Ill Patients in the MAGELLAN and MARINER Trials
Bibliographic record
Abstract
BACKGROUND: Extended thromboprophylaxis has not been widely implemented in acutely ill medical patients because of bleeding concerns. The MAGELLAN (Multicenter, Randomized, Parallel Group Efficacy and Safety Study for the Prevention of Venous Thromboembolism in Hospitalized Medically Ill Patients Comparing Rivaroxaban With Enoxaparin) and MARINER (Medically Ill Patient Assessment of Rivaroxaban Versus Placebo in Reducing Post-Discharge Venous Thrombo-Embolism Risk) trials evaluated whether rivaroxaban compared with enoxaparin or placebo could prevent venous thromboembolism without increased bleeding. We hypothesized that patients with major bleeding but not those with nonmajor clinically relevant bleeding would be at an increased risk of all-cause mortality (ACM). METHODS: We evaluated all bleeding events in patients taking at least 1 dose of study drug and their association with ACM in 4 mutually exclusive groups: (1) no bleeding, or first event was (2) nonmajor clinically relevant bleeding, (3) major bleeding, or (4) trivial bleeding. Using a Cox proportional hazards model adjusted for differences in baseline characteristics associated with ACM, we assessed the risk of ACM after such events. RESULTS: =0.021). Major bleeding was associated with a higher incidence of ACM in both studies, whereas trivial bleeding was not associated with ACM in either study. CONCLUSIONS: Patients with major bleeding had an increased risk of ACM, whereas nonmajor clinically relevant bleeding was not consistently associated with an increased risk of death. These results inform the risk-benefit calculus of extended thromboprophylaxis in medically ill patients. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: MAGELLAN, NCT00571649. URL: https://www. CLINICALTRIALS: gov; Unique identifier: MARINER, NCT02111564.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".