Racial-Ethnic Differences in Health-Related Quality of Life among Adults and Children with Glomerular Disease
Bibliographic record
Abstract
INTRODUCTION: Disparities in health-related quality of life (HRQOL) have been inadequately studied in patients with glomerular disease. The aim of this study was to identify relationships between race/ethnicity, socioeconomic status, disease severity, and HRQOL in an ethnically and racially diverse cohort of patients with glomerular disease. METHODS: Cure Glomerulonephropathy (CureGN) is a multinational cohort study of patients with biopsy-proven glomerular disease. Associations between race/ethnicity and HRQOL were determined by the following: 1. Missed school or work due to kidney disease; 2. Responses to Patient Reported Outcomes Measurement Information System (PROMIS) questionnaires. We adjusted for demographics, socioeconomic status, and disease characteristics using multivariable logistic and linear regression. RESULTS: Black and Hispanic participants had worse socioeconomic status and more severe glomerular disease than White or Asian participants. Black adults missed work or school most frequently due to kidney disease (30% versus 16-23% in the other three groups, p=0.04), and had the worst self-reported global physical health (median score 44.1 versus 48.0-48.2, p<0.001) and fatigue (53.8 versus 48.5-51.1, p=0.002), compared to other racial/ethnic groups. However, these findings were not statistically significant with adjustment for socioeconomic status and disease severity, both of which were strongly associated with HRQOL in adults. Among children, disease severity but not race/ethnicity or socioeconomic status were associated with HRQOL. CONCLUSIONS: Among patients with glomerular disease enrolled in CureGN, the worse HRQOL reported by Black adults was attributable to lower socioeconomic status and more severe glomerular disease. No racial/ethnic differences in HRQOL were observed in children.
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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 | 0.001 |
| 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".