Long‐term Follow‐up After Pediatric Liver Transplantation
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to describe long-term growth postpediatric liver transplantation and to conduct bivariate and multivariate analysis of factors that may predict post-transplantation growth in children who received a liver transplant from January 1999 to December 2008 at the Hospital for Sick Children. METHODS: A retrospective cohort study was conducted with follow-up of up-to 10 years post-transplantation. Mean height and weight z scores and annual differences in mean z scores were plotted against time after transplantation. A 1-way analysis of variance was conducted. Multivariate and univariate Cox proportional hazards analyses were conducted to determine factors associated with reaching the 50th and 25th percentiles for height. RESULTS: A total of 127 children met eligibility criteria. The mean height z score at time of transplantation was -2.21 which by the second year post-transplantation increased significantly to -0.66 (mean increase of 1.55 standard deviation units). There were no further significant increases in mean height z score from 2 years post-transplantation until the end of follow-up at year 10. In multivariate analysis, height at transplant was the most important predictor of linear growth post-transplantation. CONCLUSIONS: Children who underwent liver transplantation had significant catch-up growth in the first 2 years post-transplantation followed by a plateau phase. Increased height z-score at transplantation is the most important predictor of long-term growth.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".