Revision of MELD to Include Serum Albumin Improves Prediction of Mortality on the Liver Transplant Waiting List
Bibliographic record
Abstract
BACKGROUND: Allocation of donor livers for transplantation in most regions is based on the Model for End-Stage Liver Disease (MELD) or MELD-sodium (MELDNa). Our objective was to assess revisions to MELD and MELDNa that include serum albumin for predicting waiting list mortality. METHODS: Adults registered for liver transplantation in the United States (2002-2007) were identified from the United Network for Organ Sharing (UNOS) database. Cox regression was used to determine the association between serum albumin and 3-month mortality, and to derive revised MELD and MELDNa scores incorporating albumin ('MELD-albumin' and '5-variable MELD [5vMELD]'). RESULTS: Among 40,393 patients, 9% died and 24% underwent transplantation within 3 months of listing. For serum albumin concentrations between 1.0 and 4.0 g/dL, a linear, inverse relationship was observed between albumin and 3-month mortality (adjusted hazard ratio per 1 g/dL reduction in albumin: 1.44; 95% CI 1.35-1.54). The c-statistics for 3-month mortality of MELD-albumin and MELD were 0.913 and 0.896, respectively (P<0.001); 5vMELD was superior to MELDNa (c-statistics 0.922 vs. 0.912, P<0.001). The potential benefit of 5vMELD was greatest in patients with low MELD (<15). Among low MELD patients who died, 27% would have gained ≥10 points with 5vMELD over MELD versus only 4-7% among low MELD survivors and high MELD (≥15) candidates (P<0.0005). CONCLUSION: Modification of MELD and MELDNa to include serum albumin is associated with improved prediction of waiting list mortality. If validated and shown to be associated with reduced mortality, adoption of 5vMELD as the basis for liver allograft allocation may improve outcomes on the liver transplant waiting list.
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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".