Risk Markers of Juvenile Idiopathic Arthritis-associated Uveitis in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To characterize the epidemiology and clinical course of children with juvenile idiopathic arthritis-associated uveitis (JIA-U) in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry and explore differences between African American (AA) and non-Hispanic white (NHW) children. METHODS: There were 4983 children with JIA enrolled in the CARRA Registry. Of those, 3967 NHW and AA children were included in this study. Demographic and disease-related data were collected from diagnosis to enrollment. Children with JIA were compared to those with JIA-U. Children with JIA-U were also compared by race. RESULTS: There were 459/3967 children (11.6%) with JIA-U in our cohort with a mean age (SD) of 11.4 years (± 4.5) at enrollment. Compared to children with JIA, they were younger at arthritis onset, more likely to be female, had < 5 joints involved, had oligoarticular JIA, and were antinuclear antibody (ANA)-positive, rheumatoid factor (RF)-negative, and anticitrullinated protein antibody-negative. Predictors of uveitis development included female sex, early age of arthritis onset, and oligoarticular JIA. Polyarticular RF-positive JIA subtype was protective. Nearly 3% of children with JIA-U were AA. However, of the 220 AA children with JIA, 6% had uveitis; in contrast, 12% of the 3721 NHW children with JIA had uveitis. CONCLUSION: In the CARRA registry, the prevalence of JIA-U in AA and NHW children is 11.6%. We confirmed known uveitis risk markers (ANA positivity, younger age at arthritis onset, and oligoarticular JIA). We describe a decreased likelihood of uveitis in AA children and recommend further exploration of race as a risk factor in a larger population of AA children.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it