Neighborhood Poverty, Racial Composition and Renal Transplant Waitlist
Bibliographic record
Abstract
To date, no study has characterized the association between neighborhood poverty, racial composition and deceased donor kidney waitlist. Using the United States Renal Data System data linked to 2000 U.S. Census Data, we examined Whites (n = 152 788) and Blacks (n = 130 300) initiating dialysis between January 2000 and December 2006. Subjects' neighborhoods were divided into nine strata based on the percent of Black residents and percent poverty. Cox proportional hazards were used to determine the association between time to waitlist and neighborhood characteristics after adjusting for demographics and comorbid conditions. Individuals from poorer neighborhoods had a consistently lower likelihood of being waitlisted. This association was synergistic with neighborhood racial composition for Blacks, but not for Whites. Blacks in poor, predominantly Black neighborhoods (adjusted hazard ratio [HR] 0.57, 95% confidence intervals [CI] 0.53-0.62) were less likely to appear on transplant waitlist than those in wealthy, predominantly Black neighborhoods (HR 0.80, CI 0.67-0.96) and poor, predominantly White neighborhoods (HR 0.79, CI 0.70-0.89). All were all less likely to be waitlisted than their Black counterparts in wealthy, predominantly White or mixed neighborhoods (p < 0.05). Interventions targeted at individuals in poor and minority neighborhoods may represent an opportunity to improve equitable access to the deceased donor kidney waitlist.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".