Utility of MELD and Child-Turcotte-Pugh scores and the Canadian waitlisting algorithm in predicting short-term survival after liver transplant.
Bibliographic record
Abstract
BACKGROUND: The Model for End-Stage Liver Disease (MELD) and Child-Turcotte-Pugh (CTP) scores are important predictors for survival after liver transplantation (LT). The objective of this study was to compare the utility of MELD and CTP scores with Canadian waitlisting algorithm in transplantation (CanWAIT) status for predicting 90-day survival after LT. METHODS: Retrospectively, we analyzed all 228 liver transplants performed in adults by the Atlantic Liver Transplant Program since 1985. These cases included combined transplants, retransplants and those after fulminant liver failure. MELD and CTP scores were calculated, and CanWAIT status and waiting time on the day of LT determined. We used c-statistic for 90-day outcome as the endpoint (survival), comparing areas under the receiver operating characteristic (ROC) curves for MELD and CTP scores and CanWAIT status. RESULTS: Mean (and standard deviation [SD]) MELD score was 18 (SD 12); CTP score, 10 (SD 3); and waiting time, 97 (SD 132) days. At the time of LT, 54% were in CanWAIT status 1; 4% in 1T; 14% in 2; 11% in 3; 6% in 3F; 4% in 4; and 7% in status 4F. Overall 90-day survival was 80% (95% confidence interval [CI] 75%-85%), exceeding the predicted survival by MELD scale with transplant of only 51% (CI 47%-55%). By c-statistic, CanWAIT is a clinically relevant predictor of 90-day outcomes in LT. By multivariate regression analysis, only CanWAIT status and age were found to have independent associations for short-term outcomes after LT. INTERPRETATION: CanWAIT status stratifies LT patients better and predicts short-term outcome more accurately than MELD or CTP scores, and so should not be replaced by MELD or CTP scores. This observation should be confirmed by a prospective and multicentre study 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".