A Multivariate Analysis of Pre-, Peri-, and Post-Transplant Factors Affecting Outcome After Pediatric Liver Transplantation
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to identify significant, independent factors that predicted 6 month patient and graft survival after pediatric liver transplantation. SUMMARY BACKGROUND DATA: The Studies of Pediatric Liver Transplantation (SPLIT) is a multicenter database established in 1995, of currently more than 4000 US and Canadian children undergoing liver transplantation. Previous published analyses from this data have examined specific factors influencing outcome. This study analyzes a comprehensive range of factors that may influence outcome from the time of listing through the peri- and postoperative period. METHODS: A total of 42 pre-, peri- and posttransplant variables evaluated in 2982 pediatric recipients of a first liver transplant registered in SPLIT significant at the univariate level were included in multivariate models. RESULTS: In the final model combining all baseline and posttransplant events, posttransplant complications had the highest relative risk of death or graft loss. Reoperation for any cause increased the risk for both patient and graft loss by 11 fold and reoperation exclusive of specific complications by 4 fold. Vascular thromboses, bowel perforation, septicemia, and retransplantation, each independently increased the risk of patient and graft loss by 3 to 4 fold. The only baseline factor with a similarly high relative risk for patient and graft loss was recipient in the intensive care unit (ICU) intubated at transplant. A significant center effect was also found but did not change the impact of the highly significant factors already identified. CONCLUSIONS: We conclude that the most significant factors predicting patient and graft loss at 6 months in children listed for transplant are posttransplant surgical complications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".