Racial disparities in the incidence and risk factors of major bleeding during extended anticoagulant therapy for venous thromboembolism
Bibliographic record
Abstract
BACKGROUND: Guidelines recommend extended anticoagulation after a first unprovoked venous thromboembolism (VTE) for individuals at low risk of bleeding. However, racial disparities in bleeding risks during extended treatment remain understudied. OBJECTIVES: To compare risks of anticoagulant-associated bleeding and performance of a risk assessment model by racial group during extended VTE treatment. METHODS: We analyzed 2 prospective cohorts of patients (223 Black participants and 4314 White participants) with a first unprovoked/weakly provoked VTE who continued anticoagulation after ≥3 months of initial treatment. Primary outcome was adjudicated International Society on Thrombosis and Haemostasis-defined major bleeding. Secondary outcomes included intracranial hemorrhage, fatal bleeding, and clinically relevant nonmajor bleeding. We determined incidence and hazard ratios (HRs) by race, then adjusted for bleeding risk factors that included the Creatinine, Hemoglobin, Age, antiPlatelet model. RESULTS: Black participants had higher prevalence of bleeding risk factors and a 1.9-fold higher risk of major bleeding (HR, 1.87; 95% CI, 1.04-3.36) compared with White participants. Adjustment attenuated racial difference for major bleeding but not intracranial hemorrhage (adjusted HR, 2.35; 95% CI, 1.23-4.48). Among those classified as low risk by Creatinine, Hemoglobin, Age, antiPlatelet model, Black participants had numerically higher major bleeding incidence than White participants (2.5 vs 1.1 per 100 person-years). We did not observe racial disparities in fatal bleeding or clinically relevant nonmajor bleeding. CONCLUSION: Black individuals on extended anticoagulation have higher risk of major bleeding compared with White individuals. This effect appears to persist in those classified as low risk for bleeding. Risk assessment models for anticoagulant-associated bleeding that are generalizable to racialized populations are needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".