Relationship between updated MELD and prognosis in alcohol-associated hepatitis: Opportunities for more efficient trial design
Bibliographic record
Abstract
BACKGROUND: Alcohol-associated hepatitis (AH) is associated with significant mortality. Model for End-Stage Liver Disease (MELD) score is used to predict short-term mortality and aid in treatment decisions. MELD is frequently updated in the course of AH. However, once the most updated MELD is known, it is uncertain if previous ones still have prognostic value, which might be relevant for transplant allocation and trial design. We aimed to investigate the predictive performance of updated MELDs in a prospectively collected cohort of patients with AH by the InTeam consortium. METHODS: Three hundred seven patients (with 859 MELD values within 60 d of admission) fulfilled the inclusion criteria. The main endpoint was time to death or transplant up to 90 days. We used a joint model approach to assess the predictive value of updated MELDs. RESULTS: Updated MELD measurements had a strong prognostic value for death/transplant (HR: 1.20, 95% CI: 1.14-1.27) (p < 0.0001). Previous MELD values did not add predictive value to the most current MELD. We also showed that MELD at day 28 (MELD28) had a significant predictive value for subsequent mortality/transplant in a landmark analysis (HR: 1.18, 95% CI: 1.12-1.23). We show that the use of an ordinal scale including death, transplant, and MELD28 as a trial outcome could substantially reduce the sample size required to demonstrate short-term benefit of an intervention. CONCLUSION: We show that updated MELDs during the trajectory of AH predict subsequent mortality or the need for transplant. MELD28 inclusion in an ordinal outcome (together with death or transplant) could increase the efficiency of randomized controlled trials.
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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 | 0.270 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".