Living Donor Versus Deceased Donor Pediatric Liver Transplantation: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background. Reduced-size deceased donors and living donor liver transplantation (LDLT) can address the organ shortage for pediatric liver transplant candidates, but concerns regarding technical challenges and the risk of complications using these grafts have been raised. The aim of this study was to compare outcomes for pediatric LDLT and deceased donor liver transplantation (DDLT) via systematic review. Methods. A systematic literature search was performed to identify studies reporting outcomes of pediatric (<18 y) LDLT and DDLT published between 2005 and 2019. A meta-analysis was conducted to examine peri- and postoperative outcomes using fixed- and random-effects models. Results. Overall, 2518 abstracts were screened, and 10 studies met criteria for inclusion. In total, 1622 LDLT and 6326 DDLT pediatric patients from 4 continents were examined. LDLT resulted in superior patient survival when compared with DDLT at 1, 3, and 5 y post-LT (1-y hazard ratio: 0.58, 95% confidence interval [CI] 0.47-0.73, P < 0.0001). Similarly, LDLT resulted in superior graft survival at all time points post-LT when compared with DDLT (1-y hazard ratio: 0.56 [95% CI 0.46-0.68], P < 0.0001]. The OR for vascular complications was 0.73 (95% CI 0.39-1.39) and 1.31 (95% CI 0.92-1.86) for biliary complications in LDLT compared with DDLT, whereas LDLT was associated with lower rates of rejection (OR: 0.66 [95% CI 0.45-0.96], P = 0.03). Conclusions. This meta-analysis demonstrates that LDLT may offer many advantages when compared with DDLT in children and suggests that LDLT should continue to be expanded to optimize outcomes for pediatric LT 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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".