Efficiency of machine perfusion in pediatric liver transplantation
Bibliographic record
Abstract
Liver transplantation is the only life-saving procedure for children with end-stage liver disease. The field is however heterogenic with various graft types, recipient age, weight, and underlying diseases. Despite recently improved overall outcomes and the expanded use of living donors, waiting list mortality remains unacceptable, particularly in small children and infants. Based on the known negative effects of elevated donor age, higher body mass index, and prolonged cold ischemia time, the number of available donors for pediatric recipients is limited. Machine perfusion has regained significant interest in the adult liver transplant population during the last decade. Ten randomized controlled trials are published with an overall advantage of machine perfusion techniques over cold storage regarding postoperative outcomes, including graft survival. The concept of hypothermic oxygenated perfusion (HOPE) was the first and only perfusion technique used for pediatric liver transplantation today. In 2018 the first pediatric candidate received a full-size graft donated after circulatory death with cold storage and HOPE, followed by a few split liver transplants after HOPE with an overall limited case number until today. One series of split procedures during HOPE was recently presented by colleagues from France with excellent results, reduced complications, and better graft survival. Such early experience paves the way for more systematic use of machine perfusion techniques for different graft types for pediatric recipients. Clinical reports of pediatric liver transplants with other perfusion techniques are awaited. Strong collaborative efforts are needed to explore the effect of perfusion techniques in this vulnerable population impacting not only the immediate posttransplant outcome but the development and success of an entire life.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".