Geographic Determinants of Access to Pediatric Deceased Donor Kidney Transplantation
Bibliographic record
Abstract
Children receive priority in the allocation of deceased donor kidneys for transplantation in the United States, but because allocation begins locally, geographic differences in population and organ supply may enable variation in pediatric access to transplantation. We assembled a cohort of 3764 individual listings for pediatric kidney transplantation in 2005-2010. For each donor service area, we assigned a category of short (<180 days), medium (181-270 days), or long (>270 days) median waiting time and calculated the ratio of pediatric-quality kidneys to pediatric candidates and the percentage of these kidneys locally diverted to adults. We used multivariable Cox regression analyses to examine the association between donor service area characteristics and time to deceased donor kidney transplantation. The Kaplan-Meier estimate of median waiting time to transplantation was 284 days (95% confidence interval, 263 to 300 days) and varied from 14 to 1313 days across donor service areas. Overall, 29% of pediatric-quality kidneys were locally diverted to adults. Compared with areas with short waiting times, areas with long waiting times had a lower ratio of pediatric-quality kidneys to candidates (3.1 versus 5.9; P<0.001) and more diversions to adults (31% versus 27%; P<0.001). In multivariable regression, a lower kidney to candidate ratio remained associated with longer waiting time (hazard ratio, 0.56 for areas with <2:1 versus reference areas with ≥5:1 kidneys/candidates; P<0.01). Large geographic variation in waiting time for pediatric deceased donor kidney transplantation exists and is highly associated with local supply and demand factors. Future organ allocation policy should address this geographic inequity.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".