48390 Racial and Ethnic Disparities in Pediatric Kidney Transplantation - Has KAS made a difference?
Bibliographic record
Abstract
ABSTRACT IMPACT: Evaluate the impact that the Kidney Allocation System has had on racial and ethnic disparities in pediatric deceased donor kidney transplant recipients. OBJECTIVES/GOALS: Racial and ethnic minority pediatric transplant candidates have known disparities in access to kidney transplantation. The Kidney Allocation System (KAS), implemented in 2014, was designed in part to alleviate some of these disparities thereby making transplant more equitable. We investigated the effect of KAS on reported disparities. METHODS/STUDY POPULATION: We utilized Scientific Registry of Transplant Recipients (SRTR) data to determine differences in new waitlist registrants, deceased donor (DDKT) and living donor kidney transplants (LDKT), HLA mismatch, and allograft survival among pediatric patients of different racial and ethnic backgrounds. RESULTS/ANTICIPATED RESULTS: Black pediatric patients represented 21.3% of new waitlist registrants pre-KAS and 18.9% post-KAS. Waitlist time increased for pediatric patients of all races post-KAS with the highest increase (131 days) in Asian patients (p < 0.01). The racial distribution of DDKT pre- and post-KAS was unchanged (White 38.4% vs 38.3%, Black 24.5% vs 22.5%, Hispanic 30.6% vs 31.1%, Asian 3.7% vs 4.4%, p = 0.12). The 3-yr graft failure rate is disproportionately worse in Black children compared to other races pre- and post-KAS (White 6.8% vs 5.3%, Black 14% vs 8.7%, Hispanic 8% vs 4.5%, Asian 6.6% vs 6.7%, Other 6.5% vs 2.9%) although there is a trend towards better graft survival in the post-KAS era. Graft survival worsened in Asian children in the post-KAS era (HR 2.34,95% CI 1.05 - 5.25, p=0.038). DISCUSSION/SIGNIFICANCE OF FINDINGS: Racial and ethnic disparities in pediatric ESRD patients have not been ameliorated by KAS. Children of color have longer waitlist time and are more likely to have graft failure. Alarmingly, allograft failure rate increased in Asian patients post-KAS, which merits further evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".