Racial Disparities and Achievement of the Low Lupus Disease Activity State: A <scp>CARRA</scp> Registry Study
Bibliographic record
Abstract
OBJECTIVE: Differential disease control may contribute to racial disparities in outcomes of childhood-onset systemic lupus erythematosus (cSLE). We evaluated associations of race and individual- or neighborhood-level social determinants of health (SDoH) with achievement of low lupus disease activity state (LLDAS), a clinically relevant treatment target. METHODS: In this cSLE cohort study using the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry, the primary exposure was self-reported race and ethnicity, and collected SDoH included insurance status and area deprivation index (ADI). Outcomes included LLDAS, disease activity, and time-averaged prednisone exposure. Associations among race and ethnicity, SDoH, and disease activity were estimated with multivariable regression models, adjusting for disease-related and demographic factors. RESULTS: Among 540 children with cSLE, 27% identified as Black, 25% identified as White, 23% identified as Latino/a, 11% identified as Asian, 9% identified as more than one race, and 5% identified as other. More Black children (41%) lived in neighborhoods of highest ADI compared to White children (16%). Black race was associated with lower LLDAS achievement (adjusted odds ratio 0.56, 95% confidence interval [CI] 0.38-0.82) and higher disease activity (adjusted β 0.94, 95% CI 0.11-1.78). The highest ADI was not associated with lower LLDAS achievement on adjustment for renal disease and insurance. However, renal disease was found to be a significant mediator (P = 0.04) of the association between ADI and prednisone exposure. CONCLUSIONS: Children with cSLE who identified as Black are less likely to achieve LLDAS and have a higher disease activity. Living in areas of higher ADI may relate to renal disease and subsequent prednisone exposure. Strategies to address root causes will be important to design interventions mitigating cSLE racial disparities.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".