Association between residence location and likelihood of transplantation among pediatric dialysis patients
Bibliographic record
Abstract
Many children with ESRD reside far from a kidney transplant center. It is unknown whether this geographical barrier affects likelihood of transplantation. We used data from a national ESRD database. Patients ≤ 18 yr old who started renal replacement in nine Canadian provinces during 1992-2007 were followed until death or last contact. Primary outcome was kidney transplantation (living or deceased donor). Distance between nearest pediatric transplant center and each patient's residence was categorized as: <50, 50 to <150, 150 to <300, and ≥ 300 km. Using survival analysis, we compared likelihood of transplantation between whites and non-whites living in various distance categories. Among 728 patients, 52.2% were males and 62.5% were whites. Compared to white children living < 50 km from a transplant center, white (HR, 0.73; 95% CI, 0.56-0.95) and non-white (HR, 0.66; 95% CI, 0.48-0.92) children living ≥ 300 km away were less likely to receive a transplant. Non-white children living < 50 km away (HR, 0.59; 95% CI 0, 45-0.78) were also less likely to receive a transplant compared to otherwise similar whites living < 50 km away. Although equitable access to transplantation by residence location is observed among remote-dwelling adults with ESRD, white and non-white children with ESRD living ≥ 300 km from a transplant center were less likely to receive transplants.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".