EARLY IDENTIFICATION OF LIVER TRANSPLANTATION REQUIREMENT IN ALCOHOL-ASSOCIATED HEPATITIS
Bibliographic record
Abstract
Severe alcohol‐associated hepatitis (AH) has a high risk of short-term mortality especially in those r with acute‐on‐chronic liver failure (ACLF). Delayed evaluation for liver transplantation (LT) in severe AH often worsens nutritional and functional status. This study aimed to identify early mortality predictors. In a prospective study, 981 adults with AH were enrolled from 32 centers in 14 countries (January 2015–September 2024). ACLF was classified by EASL-CLIF criteria. Primary outcomes were 30- and 90-day mortality. Competing-risk regression (LT as the competing event) and receiver-operating-characteristic (AUROC) analyses evaluated clinical scores predicting development of ACLF grades 2–3 within seven days of admission. The mean age was 48.3 ± 11.2 years, and 88.7% were male. Within the first week, 68.8% of patients had ACLF—30.1% with grade 1, 34.5% with grade 2, and 35.4% with grade 3. Overall survival rates were 84.7% at 30 days and 75.8% at 90 days. Adjusted analyses identified increasing age, infections, higher admission MELD score, and ACLF grades 2 (subdistribution hazard ratio [sHR] 1.59) and 3 (sHR 2.58) as independent predictors of 90-day mortality. The MELD score was the best predictor of developing ACLF grades 2–3 (AUROC 0.869), with MELD ≥28 showing 64% sensitivity and 90% specificity. These findings were confirmed in two external validation cohorts: a prospectively enrolled U.S. cohort (n=234) and a retrospective cohort from seven countries (n=602). ACLF and infections are key determinants of mortality in severe AH. The MELD score at admission is a robust early predictor of high‐grade ACLF, supporting its use to determine LT candidacy earlier.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".