Survival after Kidney Transplantation during Childhood and Adolescence
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Survival in pediatric kidney transplant recipients has improved over the past five decades, but changes in cause-specific mortality remain uncertain. The aim of this retrospective cohort study was to estimate the associations between transplant era and overall and cause-specific mortality for child and adolescent recipients of kidney transplants. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Data were obtained on all children and adolescents (aged <20 years) who received their first kidney transplant from 1970 to 2015 from the Australian and New Zealand Dialysis and Transplant Registry. Mortality rates were compared across eras using Cox regression, adjusted for confounders. RESULTS: A total of 1810 recipients (median age at transplantation 14 years, 58% male, 52% living donor) were followed for a median of 13.4 years. Of these, 431 (24%) died, 174 (40%) from cardiovascular causes, 74 (17%) from infection, 50 (12%) from cancer, and 133 (31%) from other causes. Survival rates improved over time, with 5-year survival rising from 85% for those first transplanted in 1970-1985 (95% confidence interval [95% CI], 81% to 88%) to 99% in 2005-2015 (95% CI, 98% to 100%). This was primarily because of reductions in deaths from cardiovascular causes (adjusted hazard ratio [aHR], 0.25; 95% CI, 0.08 to 0.68) and infections (aHR, 0.16; 95% CI, 0.04 to 0.70; both for 2005-2015 compared with 1970-1985). Compared with patients transplanted 1970-1985, mortality risk was 72% lower among those transplanted 2005-2015 (aHR, 0.28; 95% CI, 0.18 to 0.69), after adjusting for potential confounders. CONCLUSIONS: Survival after pediatric kidney transplantation has improved considerably over the past four decades, predominantly because of marked reductions in cardiovascular- and infection-related deaths.
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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 | 0.000 |
| 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 | 0.000 |
| 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".