Liver Retransplantation in Children: A SPLIT Database Analysis of Outcome and Predictive Factors for Survival
Bibliographic record
Abstract
To examine outcomes and identify prognostic factors affecting survival after pediatric liver transplantation, data from 246 children who underwent a second liver transplantation (rLT) between 1996 and 2004 were analyzed from the SPLIT registry, a multi-center database currently comprised of 45 North American pediatric liver transplant programs. The main causes for loss of primary graft necessitating rLT were primary nonfunction, vascular complications, chronic rejection and biliary complications. Three-month, 1- and 2-year patient survival rates were inferior after rLT (74%, 67% and 65%) compared with primary LT (92%, 88% and 85%, respectively). Multivariate analysis of pretransplant variables revealed donor age less than 1 year, use of a technical variant allograft and INR at time of rLT as independent predictive factors for survival after rLT. Survival of patients who underwent early rLT (ErLT, <30 days after LT) was poorer than those who received rLT >30 days after LT (late rLT, LrLT): 3-month, 1- and 2-year patient survival rates 66%, 59%, and 56% versus 80%, 74% and 61%, respectively, log-rank p = 0.0141. Liver retransplantation in children is associated with decreased survival compared with primary LT, particularly, in the clinical settings of those patients requiring ErLT.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".