Survival in Pediatric Dialysis and Transplant Patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Long-term follow-up data are few in children with ESRD. We sought to describe long-term survival, assess risk factors for death, and compare survival between two time periods in pediatric ESRD patients. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We used a population-based retrospective cohort utilizing data from a national organ failure registry and from Canada's universal healthcare system. We included 843 children (ages, 0 to 18) initiating renal replacement therapy from 1992 to 2007 and followed them until death or date of last contact (median follow-up, 6.8 years; interquartile range, 3.0 to 10.6). We assessed risk factors for death and examined cause-specific mortality. RESULTS: During 5991 patient-years of follow-up, 107 (12.7%) patients died. Unadjusted cumulative survival for the cohort was: 91.7% (95% CI, 89.8 to 93.7%) at 5 years and 85.8% (95% CI, 82.8 to 88.8%) at 10 years. Among patients commencing dialysis, overall adjusted survival was poorest among those who started dialysis at age <1 year. No secular trends in survival were noted for either dialysis or transplant patients. The proportion of incident patients receiving pre-emptive transplantation increased over time. Pre-emptively transplanted patients did not demonstrate superior adjusted survival compared with those who spent >2 years on dialysis before transplant (hazard ratio, 1.53; 95% CI, 0.63 to 3.67). CONCLUSIONS: No significant improvements in survival were observed among ESRD patients over the study period. Time with transplant function had the strongest association with survival. Pre-emptive transplantation was not associated with improved survival in adjusted models.
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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 | 0.001 | 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".