Predictors of Non-Variceal Hemorrhage in a National Cohort of Patients With Chronic Liver Disease
Bibliographic record
Abstract
Background: Non-variceal hemorrhage in patients with chronic liver disease (CLD) increases morbidity, mortality, and healthcare costs. There are limited data on risk factors for non-variceal hemorrhage in the CLD population. The aim of this study was to assess the predictive value of various clinical and laboratory parameters for non-variceal hemorrhage in CLD patients. Methods: We conducted a retrospective cohort study of US veterans diagnosed with CLD between 2002 and 2018 within the Veterans Health Administration database. We derived candidate variables from existing risk prediction models for hemorrhage, risk calculators for severity of liver disease, Charlson index of prognostic comorbidities, and prior literature. We used a competing risk analysis to study the relationship between putative risk factors and incidence of non-variceal hemorrhage in patients with CLD. Results: Of 15,183 CLD patients with no history of cancer or anticoagulation use, 674 experienced non-variceal hemorrhage within 1 year of CLD diagnosis. In multivariable analysis, 11 of the 26 candidate variables independently predicted non-variceal hemorrhage: race, international normalized ratio (INR) > 1.5, bilirubin ≥ 2 mg/dL, albumin ≤ 3.5 g/dL, anemia, alcohol abuse, antiplatelet therapy, chronic kidney disease, dementia, proton pump inhibitor prescription, and recent infection. Conclusions: In this study of almost 15,000 veterans, risk factors for non-variceal bleeding within the first year after diagnosis of CLD included non-Caucasian race, laboratory parameters indicating severe liver disease and recent infection in addition to the risk factors for bleeding observed in a general non-CLD population.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".