Risk Markers of Juvenile Idiopathic Arthritis-associated Uveitis in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry
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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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".