The Society of Pediatric Liver Transplantation ( <scp>SPLIT</scp> ): 2023 Registry Status
Bibliographic record
Abstract
BACKGROUND: The Society of Pediatric Liver Transplantation (SPLIT) has undergone tremendous growth with > 45 sites contributing data focusing on improving outcomes in pediatric liver transplantation (LT). We report and compare outcomes from the SPLIT Registry. METHODS: Patients < 18 years with first-time LT only enrolled into the SPLIT Registry between 2011 and 2023 were included. Data was stratified into eras from the last published registry update (era 1: 2011-2018, era 2: 2018-2023). RESULTS: Three thousand five hundred four participants from 47 centers were included (era 1: n = 2159; era 2: n = 1345). Age distribution differed with more children < 1 year. of age at LT in era 2 (era 1: 29% vs. era 2: 33%, p = 0.01). Indications for LT were similar, with biliary atresia (38.8%) and metabolic disease (16.0%) being most common. Exception point use was higher in era 2 (era 1: 45% vs. era 2: 56%, p < 0.001). No difference in graft type (deceased: 81% era 1 vs. 78% era 2, p = 0.78), patient survival at 90 days (era 1: 98.7% vs. era 2: 98.3%), 1 year (era 1: 97.2 vs. era 2: 96.8%), or 3 years (era 1: 95.3% vs. era 2: 95.2%) was noted. Rate of hepatic artery thrombosis was lower in era 2 (era 1: 7% vs. era 2: 5%, p = 0.02). CONCLUSIONS: Trends in pediatric LT within SPLIT note similar LT indications and graft type, higher utilization of exception points, and lower HAT rates despite transplanting more children < 10 kg. This data underscores the evolution of pediatric LT toward higher survivability and overall patient outcomes.
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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 | 0.001 |
| 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".