Validation of the five‐variable Model for End‐stage Liver Disease (5vMELD) for prediction of mortality on the liver transplant waiting list
Bibliographic record
Abstract
BACKGROUND: Modifications to the Model for End-Stage Liver Disease (MELD) have been proposed to improve prioritization of liver transplant (LT) candidates. Using a U.S. database, we derived a revised MELD including sodium and albumin [5-variable MELD (5vMELD)] that improved prediction of waiting list mortality. Our objectives were to confirm the association between hypoalbuminaemia and mortality and to externally validate 5vMELD in Canadian LT candidates. METHODS: Among adults registered on the LT waiting list at the University of Alberta (01/2000-10/2009), Cox regression determined the association between albumin and 1-year waiting list mortality. The discrimination of MELD, MELDNa and 5vMELD for predicting 1-year mortality were compared using c-statistics. RESULTS: Among 677 patients, 17% died and 51% underwent LT within 1 year of listing. Median serum albumin was 3.1 g/dl (IQR 2.6-3.6) and 70% of patients were hypoalbuminaemic (albumin <3.5 g/dl). One-year mortality in patients with normal serum albumin and hypoalbuminaemia were 14% and 29% respectively (P = 0.004). For patients with serum albumin between 2.0 and 4.0 g/dl, an approximately linear, inverse relationship was observed between albumin and 1-year mortality [adjusted hazard ratio (HR) 1.45; 95% CI 1.03-2.03; P = 0.03]. For this outcome, the c-statistic of 5vMELD (0.778) was superior to those of MELD (0.754) and MELDNa (0.765) (both P ≤ 0.05). CONCLUSIONS: Hypoalbuminaemia is an independent predictor of mortality on the LT waiting list. Compared with MELD and MELDNa, 5vMELD improves prediction of mortality suggesting that modification of these scores to include serum albumin should be considered as a means of prioritizing LT candidates.
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 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".