Patients with severe acute‐on‐chronic liver failure are disadvantaged by model for end‐stage liver disease‐based organ allocation policy
Bibliographic record
Abstract
BACKGROUND: Mortality for patients with acute-on-chronic liver failure (ACLF) may be underestimated by the model for end-stage liver disease-sodium (MELD-Na) score. AIM: To assess waitlist outcomes across varying grades of ACLF among a cohort of patients listed with a MELD-Na score ≥35, and therefore having similar priority for liver transplantation. METHODS: We analysed the United Network for Organ Sharing (UNOS) database, years 2010-2017. Waitlist outcomes were evaluated using Fine and Gray's competing risks regression. RESULTS: We identified 6342 candidates at listing with a MELD-Na score ≥35, of whom 3122 had ACLF-3. Extra-hepatic organ failures were present primarily in patients with four to six organ failures. Competing risks regression revealed that candidates listed with ACLF-3 had a significantly higher risk for 90-day waitlist mortality (Sub-hazard ratio (SHR) = 1.41; 95% confidence interval [CI] 1.12-1.78) relative to patients with lower ACLF grades. Subgroup analysis of ACLF-3 revealed that both the presence of three organ failures (SHR = 1.40, 95% CI 1.20-1.63) or four to six organ failures at listing (SHR = 3.01; 95% CI 2.54-3.58) was associated with increased waitlist mortality. Candidates with four to six organ failures also had the lowest likelihood of receiving liver transplantation (SHR = 0.61, 95% CI 0.54-0.68). The Share 35 rule was associated with reduced 90-day waitlist mortality among the full cohort of patients listed with ACLF-3 and MELD-Na score ≥35 (SHR = 0.59; 95% CI 0.49-0.70). However, Share 35 rule implementation was not associated with reduced waitlist mortality among patients with four to six organ failures (SHR = 0.76; 95% CI 0.58-1.02). CONCLUSIONS: The MELD-Na score disadvantages patients with ACLF-3, both with and without extra-hepatic organ failures. Incorporation of organ failures into allocation policy warrants further exploration.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".