A model including standardized weight improved predicting waiting list mortality in adolescent liver transplant candidates: A US national study
Bibliographic record
Abstract
The Model for End-Stage Liver Disease (MELD) score has been employed to identify adolescents eligible for liver transplantation since 2004. However, the optimal model for prioritizing adolescent candidates is uncertain. In our study, we aimed at evaluating the value of adding anthropometric variables to liver transplantation allocation models among adolescents. We conducted a retrospective cohort study using the data from the Organ Procurement and Transplantation Network Standard Transplant Analysis and Research to identify adolescent patients registered on the liver transplant waiting list in the United States between January 1, 2003, and December 31, 2022. Adolescents (12-17 y) who were listed for their first liver transplantation were included. We evaluated the performance of different models including pediatric end-stage liver disease with Na and creatinine, MELD, and MELD 3.0. Furthermore, we evaluated whether adding anthropometric variables ( z -score for weight and height) would improve the models' performance for our primary outcome (mortality at 90 days after listing). We identified 1421 eligible adolescent patients. Adding a z -score of weight (MELD-TEEN) improved the performance and discrimination of the MELD score. The final model including weight z -score (MELD-TEEN) had better discriminative power compared to MELD 3.0 and pediatric end-stage liver disease with Na and creatinine in the overall cohort and in different age groups (ages 12-14 and 15-17). MELD-TEEN could improve the accuracy of allocation of liver transplants among adolescents by incorporating the weight z -score compared to MELD 3.0 and pediatric end-stage liver disease with Na and creatinine.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".