Defining Risk in Alcohol-Associated Liver Disease Using the Model for End-Stage Liver Disease
Bibliographic record
Abstract
INTRODUCTION: Alcohol-associated liver disease (ALD) is a common cause of morbidity and premature mortality. Most prognostic scores have been defined in the short term. We used a large retrospective cohort of patients with ALD to describe the natural history of ALD and to define risk prediction in the longer term, taking nonliver mortality into account. METHODS: The WALDO cohort includes 734 patients with biopsy-proven ALD. Prognostic scores were assessed with dynamic area under the curve and C-index. Risk estimates for morbidity and mortality were derived for the model for end-stage liver disease (MELD) and validated in an external cohort. RESULTS: During a median follow-up of 4.9 years, 240 patients died from liver disease or underwent liver transplantation (LT), and 114 patients died from nonliver causes. Outcomes varied across the spectrum of ALD: The cumulative incidence of liver-related death or LT in people with decompensated cirrhosis or alcohol-associated hepatitis was 47% and 40%, respectively, compared with 7.4% in patients without cirrhosis and 13% in compensated cirrhosis. MELD was the best predictor of outcomes: (area under the curve for mortality/LT at 1 year was 0.853), although MELD3.0 and the Child-Turcotte-Pugh score performed similarly. Risk of liver-related outcomes were tabulated for integer values of the MELD score. Risk estimates based on the MELD were well calibrated in an external cohort. DISCUSSION: These data illustrate the natural history of ALD and define the risks of outcomes based on the MELD score across the spectrum of disease.
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.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".