Are MELD and MELDNa Still Reliable Tools to Predict Mortality on the Liver Transplant Waiting List?
Bibliographic record
Abstract
Liver transplantation is the only curative treatment for end-stage liver disease. Unfortunately, the scarcity of donor organs and the increasing pool of potential recipients limit access to this life-saving procedure. Allocation should account for medical and ethical factors, ensuring equal access to transplantation regardless of recipient's gender, race, religion, or income. Based on their short-term prognosis prediction, model for end-stage liver disease (MELD) and MELD sodium (MELDNa) have been widely used to prioritize patients on the waiting list for liver transplantation resulting in a significant decrease in waiting list mortality/removal. Recent concern has been raised regarding the prognostic accuracy of MELD and MELDNa due, in part, to changes in recipients' profile such as body mass index, comorbidities, and general condition, including nutritional status and cause of liver disease, among others. This review aims to provide a comprehensive view of the current state of MELD and MELDNa advantages and limitations and promising alternatives. Finally, it will explore future options to increase the donor pool and improve donor-recipient matching.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".