Race, Ethnicity, and Disease Outcomes in Juvenile Idiopathic Arthritis: A Cross-sectional Analysis of the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry
Bibliographic record
Abstract
OBJECTIVE: To measure the associations between self-reported race and ethnicity and disease outcomes, including joint damage, pain, and functional ability, in children with juvenile idiopathic arthritis (JIA). METHODS: A cross-sectional analysis of children with JIA enrolled in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry between May 2010 and March 2012. Mann-Whitney U test and chi-square testing were used to compare patient characteristics between race (white, African American, or Asian) and ethnicity (Non-Hispanic and Non-Latino; Hispanic or Latino) categories. Logistic regression was used to measure the associations between each race or ethnicity category and the outcome of interest. RESULTS: Race category was available for 4292 of 4682 children (93% white, 5% African American, Asian 3%). Ethnicity data were available for 4644 (11% Hispanic or Latino). African American children with polyarticular-course JIA had an elevated OR for joint damage on radiographic imaging compared to white children (OR 1.9, 95% CI 1.0-3.1; p = 0.04). Hispanic/Latino children had increased odds of having disability scores > 75th percentile (OR 1.5, 95% CI 1.1-2.1; p < 0.01) compared to non-Hispanic/Latino children; however, these odds were no longer significant when the cohort was limited to children with polyarticular-course JIA. Asian children had decreased odds of higher pain and functional disability compared to white children (p < 0.05). CONCLUSION: Race and ethnicity were variably associated with joint damage, pain, and functional ability. Understanding outcome variation between different race and ethnicity groups may help to optimize care for children with JIA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".