Event rates and risk factors for venous thromboembolism and major bleeding in a population of hospitalized adult patients with acute medical illness receiving enoxaparin thromboprophylaxis
Bibliographic record
Abstract
BACKGROUND: We aimed to describe the event rates and risk-factors for symptomatic venous thromboembolism (VTE) and major bleeding in a population of hospitalized acutely ill medical patients. METHODS: Patients ≥40 years old and hospitalized for acute medical illness who initiated enoxaparin prophylaxis were selected from the US Optum research database. Rates of symptomatic VTE and major bleeding at 90-days were estimated via the Kaplan-Meier (KM) method. Risk factors were identified via the Cox proportional hazards model. RESULTS: A total of 123,022 patients met the selection criteria. The KM rates of VTE and major bleeding at 90-days were 3.5 % and 2.2 %, respectively. Among subgroups, the risk of VTE varied from 3.0 % in patients with ischemic stroke to 6.9 % in patients with a cancer-related hospitalization, and the risk of major bleeding varied from 1.9 % in patients with inflammatory conditions to 3.6 % in patients with ischemic stroke. Key risk factors for VTE were prior VTE (HR=4.15, 95 % confidence interval [CI] 3.80-4.53), cancer-related hospitalization (HR=2.35, 95 % CI 2.10-2.64), and thrombophilia (HR=1.64, 95 % CI 1.29-2.08). Key risk factors for major bleeding were history of major bleeding (HR=2.17, 95 % CI 1.72-2.74), history of non-major bleeding (HR=2.46, 95 % CI 2.24-2.70), and hospitalization for ischemic stroke (2.42, 95 % CI 2.11-2.78). CONCLUSION: There is substantial heterogeneity in the event rates for VTE and major bleeding in acute medically ill patients. History of VTE and cancer related hospitalization represent profiles with a high risk of VTE, where continued VTE prophylaxis may be warranted.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".