Risk Factors for Mortality in Patients with Alcoholic Hepatitis and Assessment of Prognostic Models: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Severe alcoholic hepatitis (AH) is associated with a substantial risk for short-term mortality. OBJECTIVES: To identify prognostic factors and validate well-known prognostic models in a Canadian population of patients hospitalized for AH. METHODS: In the present retrospective study, patients hospitalized for AH in Calgary, Alberta, between January 2008 and August 2012 were included. Stepwise logistic regression models identified independent risk factors for 90-day mortality, and the discrimination of prognostic models (Model for End-stage Liver Disease [MELD] and Maddrey discriminant function [DF]) were examined using areas under the ROC curves. RESULTS: A total of 122 patients with AH were hospitalized during the study period; the median age was 49 years (interquartile range [IQR] 42 to 55 years) and 60% were men. Median MELD score and Maddrey DF on admission were 21 (IQR 18 to 24) and 45 (IQR 26 to 62), respectively. Seventy-three percent of patients received corticosteroids and⁄or pentoxifylline, and the 90-day mortality was 17%. Independent predictors of mortality included older age, female sex, international normalized ratio, MELD score and Maddrey DF (all P<0.05). For discrimination of 90-day mortality, the areas under the ROC curves of the prognostic models (MELD 0.64; Maddrey DF 0.68) were similar (P>0.05). At optimal cut-offs of ≥22 for MELD score and ≥37 for Maddrey DF, both models excluded death with high certainty (negative predictive values 90% and 96%, respectively). CONCLUSIONS: In patients hospitalized for AH, well-known prognostic models can be used to predict 90-day mortality, particularly to identify patients with a low risk for death.
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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) | 0.001 | 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 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".