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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".