Similarities and Differences in Allocation Policies for Pediatric Liver Transplantation Across the World
Bibliographic record
Abstract
OBJECTIVES: We aimed to investigate national allocation policies for pediatric liver transplantation (LT). METHOD: A survey was prepared by the European Society for Paediatric Gastroenterology Hepatology and Nutrition Hepatology Committee in collaboration with the North American Studies of Pediatric Liver Transplantation consortium. The survey was sent to pediatric hepatologists and transplant surgeons worldwide. National data were obtained from centrally based registries. RESULTS: Replies were obtained from 15 countries from 5 of the world continents. Overall donation rate varied between 9 and 35 per million inhabitants. The number of pediatric LTs was 4 to 9 per million inhabitants younger than 18 years for 13 of the 15 respondents. In children younger than 2 years mortality on the waiting list (WL) varied between 0 and 20%. In the same age group, there were large differences in the ratio of living donor LT to deceased donor LT and in the ratio of split liver segments to whole liver. These differences were associated with possible discrepancies in WL mortality. CONCLUSIONS: Similarities but also differences between countries were detected. The described data may be of importance when trying to reduce WL mortality in the youngest children.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".