State of pediatric liver transplantation in the United States and achieving zero wait list mortality with ideal outcomes: A statement from the Starzl Network for Excellence in Pediatric Transplant Surgeon's Working Group
Bibliographic record
Abstract
BACKGROUND: Liver transplant is a life-saving therapy that can restore quality life for several pediatric liver diseases. However, it is not available to all children who need one. Expertise in medical and surgical management is heterogeneous, and allocation policies are not optimally serving children. Technical variant grafts from both living and deceased donors are underutilized. METHODS: Several national efforts in pediatric liver transplant to improve access to and outcomes from liver transplant for children have been instituted and include adjustments to allocation policies, UNOS-sponsored collaborative improvement projects, and the emergence of national learning networks to study ongoing challenges in the field the Surgical Working group of the Starzl Network for Excellence in Pediatric Transplantation (SNEPT) discusses key issues and proposes potential solutions to eliminate the persistent wait list mortality that pediatric patients face. RESULTS: A discussion of the factors impacting pediatric patients' access to liver transplant is undertaken, along with a proposal of several measures to ensure equitable access to life-saving liver transplant. CONCLUSIONS: Pediatric liver transplant wait list mortality can and should be eliminated. Several measures, including collaborative efforts among centers, could be leveraged to acheive this goal.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| 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".