Racial Composition of Residential Areas Associates with Access to Pre-ESRD Nephrology Care
Bibliographic record
Abstract
Referral to a nephrologist before initiation of chronic dialysis occurs less frequently for blacks than whites, but the reasons for this disparity are incompletely understood. Here, we examined the contribution of racial composition by zip code on access and quality of nephrology care before initiation of renal replacement therapy (RRT). We retrospectively studied a cohort study of 92,000 white and black adults who initiated RRT in the United States between June 1, 2005, and October 5, 2006. The percentage of patients without pre-ESRD nephrology care ranged from 30% among those who lived in zip codes with <5% black residents to 41% among those who lived in areas with >50% black residents. In adjusted analyses, as the percentage of blacks in residential areas increased, the likelihood of not receiving pre-ESRD nephrology care increased. Among patients who received nephrology care, the quality of care (timing of care and proportion of patients who received a pre-emptive renal transplant, who initiated therapy with peritoneal dialysis, or who had a permanent hemodialysis access) did not differ by the racial composition of their residential area. In conclusion, racial composition of residential areas associates with access to nephrology care but not with quality of the nephrology care received.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".