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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it