MORTALITY TRENDS AND RISK FACTORS IN ALCOHOL-ASSOCIATED HEPATITIS: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Severe alcohol-associated hepatitis (sAH) is a life-threatening condition with high short-term mortality. Despite therapeutic advances, long-term effectiveness remains limited. We conducted a systematic review and meta-analysis to evaluate mortality trends in sAH over the past five decades. We searched PubMed, EMBASE, and Scopus through February 2024 for studies reporting 28-, 60-, and 90-day mortality in sAH. Pooled mortality estimates were calculated using mixed-effects models. Heterogeneity was assessed using the I 2 statistic, with subgroup and meta-regression analyses exploring potential modifiers. Bayesian models estimated the posterior probability distribution of mortality. Forty-five studies comprising 5,632 patients were included. Pooled mortality was 28.3% (95% CI: 22.5–34.8%) at 28 days, 38.3% (95% CI: 31.5–45.5%) at 60 days, and 48.7% (95% CI: 39.2–58.3%) at 90 days. Heterogeneity across studies was high (I 2 > 80%). Bayesian models suggested a decline in 28-day mortality from over 50% in the 1970s to approximately 25% after 2000; however, no consistent reduction in overall mortality was observed. Meta-regression showed no significant association with sex, age, mDF, or publication year, but higher MELD scores were linked to increased mortality (β = +0.20 per point; 95% CI: +0.01 to +0.39; p = 0.037). The use of corticosteroids, NAC, or G-CSF did not significantly affect mortality. Despite improved supportive care, short-term mortality in sAH remains high and unchanged over recent decades. These findings underscore the urgent need for effective treatments and support early liver transplant consideration in selected patients.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.041 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".