Living-donor availability improves pediatric patient survival in a large North American center: An intention-to-treat analysis
Bibliographic record
Abstract
Although living-donor liver transplantation (LDLT) is increasingly adopted for pediatric liver transplantation, there is limited data on whether live donation extends benefits to patients from the time of listing. This study investigated the benefits of pediatric LDLT through an intention-to-treat analysis. Pediatric candidates listed between 2001 and 2023 at a single Canadian center were categorized as pLDLT (with a potential live donor) or pDDLT (without a live donor). The primary endpoint was overall survival from the time of listing. The secondary endpoint involved the waitlist outcomes described by the probabilities of receiving liver transplantation or waitlist dropout. Among 474 candidates, 219 (46.2%) had potential live donors. The pLDLT group had a higher likelihood of receiving a liver transplantation (adjusted HR: 1.38, 95% CI: 1.16-1.64) and a lower risk of dying without a transplant (adjusted HR: 0.11, 95% CI: 0.01-0.82) compared to the pDDLT group. Survival rates from the time of listing were significantly better in the pLDLT group at 1-(98.6% vs. 87.6%), 5-(96.6% vs. 84.4%), and 10-(96.6% vs. 83.1%) years. Having a potential live donor was linked to a 72% reduction in mortality risk (adjusted HR: 0.28, 95% CI: 0.12-0.64). Although the number of patients listed annually increased over the study period, the waiting time for deceased donation shortened. This correlated with increased LDLT utilization, suggesting LDLT not only improved outcomes but also shortened wait times even for pDDLT patients. Having a potential live donor is associated with substantial survival benefit. Pediatric programs offering LDLT can expand the donor pool and decrease the waiting time for DDLT, supporting the argument for making LDLT a standard for pediatric candidates.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".