Glomerular Filtration Rate Following Pediatric Liver Transplantation—The SPLIT Experience
Bibliographic record
Abstract
Impaired kidney function is a well-recognized complication following liver transplantation (LT). Studies of this complication in children have been limited by small numbers and insensitive outcome measures. Our aim was to define the prevalence of, and identify risk factors for, post-LT kidney dysfunction in a multicenter pediatric cohort using measured glomerular filtration rate (mGFR). We conducted a cross-sectional study of 397 patients enrolled in the Studies in Pediatric Liver Transplantation (SPLIT) registry, using mGFR < 90 mL/min/1.73 m(2) as the primary outcome measure. Median age at LT was 2.2 years. Primary diagnoses were biliary atresia (44.6%), fulminant liver failure (9.8%), metabolic liver disease (16.4%), chronic cholestatic liver disease (13.1%), cryptogenic cirrhosis (4.3%) and other (11.8%). At a mean of 5.2 years post-LT, 17.6% of patients had a mGFR < 90 mL/min/1.73 m(2) . In univariate analysis, factors associated with this outcome were transplant center, age at LT, primary diagnosis, calculated GFR (cGFR) at LT and 12 months post-LT, primary immunosuppression, early post-LT kidney complications, age at mGFR, height and weight Z-scores at 12 months post-LT. In multivariate analysis, independent variables associated with a mGFR <90 mL/min/1.73 m(2) were primary immunosuppression, age at LT, cGFR at LT and height Z-score at 12 months post-LT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".