The Impact of Increased Allocation Priority for Children Awaiting Liver Transplant
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to investigate the impact of prioritizing infants, children, adolescents, and the sickest adults (Status 1) for deceased donor livers. We compared outcomes under two "SharePeds" allocation schema, which prioritize children and Status 1 adults for national sharing and enhanced access to pediatric donors or all donors younger than 35 years, to outcomes under the allocation plan approved by the Organ Procurement and Transplant Network in December 2017 (Organ Procurement and Transplantation Network [OPTN] 12-2017). METHODS: The 2017 Liver Simulated Allocation Model and Scientific Registry of Transplant Recipients data on all US liver transplant candidates and liver offers 7/2013 to 6/2016 were used to predict waitlist deaths, transplants, and post-transplant deaths under the OPTN 12-2017 and SharePeds schema. RESULTS: Prioritizing national sharing of pediatric donor livers with children (SharePeds 1) would decrease waitlist deaths for infants (<2 years, P = 0.0003) and children (2-11 years, P = 0.001), with no significant change for adults (P = 0.13). Prioritizing national sharing of all younger than 35-year-old deceased donor livers with children and Status 1A adults (SharePeds 2) would decrease waitlist deaths for infants, children, and all Status 1A/B patients (P < 0.0001 for each). SharePeds 1 and 2 would increase the number of liver transplants done in infants, children, and adolescents compared to the OPTN-2017 schema (P < 0.00005 for all age groups). Both SharePeds schema would increase the percentage of pediatric livers transplanted into pediatric recipients. CONCLUSIONS: Waitlist deaths could be significantly decreased, and liver transplants increased, for children and the sickest adults, by prioritizing children for pediatric livers and with broader national sharing of deceased donor livers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".