Validation of the Model of End-Stage Liver Disease for Liver Transplant Allocation in Alberta: Implications for Future Directions in Canada
Bibliographic record
Abstract
Background. Since 2002, the Model of End-Stage Liver Disease (MELD) has been used for allocation of liver transplants (LT) in the USA. In Canada, livers were allocated by the CanWAIT algorithm. The aim of this study was to compare the abilities of MELD, Child-Pugh (CP), and CanWAIT status to predict 3-month and 1-year mortality before LT in Canadian patients and to describe the use of MELD in Canada. Methods. Validation of MELD was performed in 320 patients listed for LT in Alberta (1998-2002). In October 2014, a survey of MELD use by Canadian LT centers was conducted. Results. Within 1 year of listing, 47 patients were removed from the waiting list (29 deaths, 18 too ill for LT). Using logistic regression, the MELD and CP were better than the CanWAIT at predicting 3-month (AUROC: 0.79, 0.78, and 0.59; p = 0.0002) and 1-year waitlist mortality (AUROC: 0.70, 0.70, and 0.55; p = 0.0023). Beginning in 2004, MELD began to be adopted by Canadian LT programs but its use was not standardized. Conclusions. Compared with the CanWAIT system, the MELD score was significantly better at predicting LT waitlist mortality. MELD-sodium (MELD-Na) has now been adopted for LT allocation in Canada.
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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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".