Role of Neighborhood Disadvantage in Patient Outcomes in Childhood Nephrotic Syndrome in the CureGN Cohort
Bibliographic record
Abstract
Background: Disparities in clinical outcomes in childhood nephrotic syndrome are not completely explained by race and genetics. We aim to examine the influence of residential neighborhood on disease activity and progression in childhood nephrotic syndrome using the Child Opportunity Index (COI). Methods: CureGN is a prospective cohort study of patients with glomerular diseases diagnosed by biopsy within 5 years prior to enrollment. CureGN pediatric participants with minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS) with census tract data were included. COI composite score and subdomain scores in education, health and environment, and social and economic were categorized into quintiles and examined for associations with development of ESKD or 40% decline in eGFR using Kaplan-Meier estimates and log-rank tests. Results: 371 children (228 MCD; 143 FSGS) with a median follow-up of 5.8 years (IQR 3.4-7.2) were included. Median age at biopsy was 8 years (IQR 4-7); 58% were White, 26% Black, and 12% Hispanic. In the subset of children with FSGS, the probability of progression to kidney failure or 40% decline differed across quintiles of the education domain COI (p=0.02). Conclusion: In a national cohort of children with nephrotic syndrome, children with FSGS who resided in areas with lower education opportunities had a higher probability of disease progression. Future work will assess potential ways to mitigate the effects of neighborhood opportunities on outcomes. Funding: NIDDK SupportTime to composite endpoint by COI levels (FSGS only)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".