Predicting Survival after Liver Transplantation Based on Pre-Transplant MELD Score: a Systematic Review of the Literature
Bibliographic record
Abstract
The model for end-stage liver disease (MELD) score is used to stratify candidates for liver transplantation based on objective measures of disease severity. MELD has been validated as a predictor of wait-list mortality in transplantation candidates and has been postulated as a predictor of post-transplant survival. The purpose of this study was to examine the predictive value of the pre-transplantation MELD score on post-transplant survival from relevant existing studies. A systematic review and critical appraisal was performed using Cochrane guidelines. PubMed, the Cochrane Library, Embase, and Web of Science were searched for articles published in the English language since 2005 using a structured search strategy. There were 3058 discrete citations identified and screened for possible inclusion. Any study examining the relationship between pre-transplant MELD and post-transplant survival in the general transplant population was included. Thirty-seven studies met these criteria and were included in the review. Studies were all case series that typically involved stratified analyses of survival by MELD. They represented 15 countries and a total of 53,691 patients. There was significant clinical heterogeneity in patient populations across studies, which precluded performance of a meta-analysis. In 15 studies, no statistically significant association between MELD and post-transplant survival was found. In the remaining 22, some association was found. Eleven studies also measured predictive ability with c-statistics. Values were below 0.7 in all but two studies, suggesting poor predictive value. In summary, while the majority of studies reported an association between pre-transplantation MELD score and post-transplant survival, they represented a low level of evidence. Therefore, their findings should be interpreted conservatively.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".