Superior Outcomes and Reduced Wait Times in Pediatric Recipients of Living Donor Liver Transplantation
Bibliographic record
Abstract
Background Living donor liver transplantation (LDLT) is increasingly used to bridge the gap between the current supply and demand imbalance for deceased donor organs to provide lifesaving liver transplantation. Methods Outcomes of 135 children who underwent LDLT were compared with 158 recipients of deceased donor liver transplantation (DDLT) at the largest pediatric liver transplant program in Canada. Results Recipients of LDLT were significantly younger than deceased donor recipients (P ≤ 0.001), less likely to require dialysis pretransplant (P < 0.002) and had shorter wait time duration when the primary indication was cholestatic liver disease (P = 0.003). The LDLT donors were either related genetically or emotionally (79%), or unrelated (21%) to the pediatric recipients. One-, 5-, and 10-year patient survival rates were significantly higher in LDLT (97%, 94%, and 94%) compared with DDLT (92%, 87%, and 80%; log-rank P = 0.02) recipients, as were graft survival rates (96%, 93%, and 93% for LDLT versus 89%, 81.4%, and 70%, respectively, for DDLT; log-rank P = 0.001). Medical and surgical complications were not statistically different between groups. Graft failure was higher in recipients of DDLT (odds ratio, 2.60; 95% confidence interval, 1.02, 6.58) than in the LDLT group after adjustment for clinical characteristics and propensity score. Conclusions Living donor liver transplantation provides superior outcomes for children and is an excellent and effective strategy to increase the chances of receiving a liver transplant.
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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 | 0.000 | 0.003 |
| 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) | 0.002 | 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".