Risk factors for late renal dysfunction after pediatric heart transplantation: A multi‐institutional study
Bibliographic record
Abstract
Feingold B, Zheng J, Law YM, Morrow WR, Hoffman TM, Schechtman KB, Dipchand AI, Canter CE and the Pediatric Heart Transplant Study Investigators. Risk factors for late renal dysfunction after pediatric heart transplantation: A multi‐institutional study. Pediatr Transplantation 2011: 15: 699–705. © 2011 John Wiley & Sons A/S. Abstract: Renal dysfunction is a major determinant of outcome after HTx. Using a large, multi‐institutional database, we sought to identify factors associated with late renal dysfunction after pediatric HTx. All patients in the PHTS database with eGFR ≥60 mL/min/1.73 m2 at one yr post‐HTx (n = 812) were analyzed by Cox regression for association with risk factors for eGFR <60 mL/min/1.73 m2 at >1 yr after HTx. Freedom from late renal dysfunction was 71% and 57% at five and 10 yr. Multivariate risk factors for late renal dysfunction were earlier era of HTx (HR 1.84; p < 0.001), black race (HR 1.42; p = 0.048), rejection with hemodynamic compromise in the first year after HTx (HR 1.74; p = 0.038), and lowest quartile eGFR at one yr post‐HTx (HR 1.83; p < 0.001). Renal function at HTx was not associated with onset of late renal dysfunction. Eleven patients (1.4%) required chronic dialysis and/or renal transplant during median follow‐up of 4.1 yr (1.5–12.6). Late renal dysfunction is common after pediatric HTx, with blacks at increased risk. Decreased eGFR at one yr post‐HTx, but not at HTx, predicts onset of late renal dysfunction. Future research on strategies to minimize late renal dysfunction after pediatric HTx may be of greatest benefit if focused on these subgroups.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".