Phenotypic Characterization of Juvenile Idiopathic Arthritis in African American Children
Bibliographic record
Abstract
OBJECTIVE: Juvenile idiopathic arthritis (JIA) affects children of all races. Prior studies suggest that phenotypic features of JIA in African American (AA) children differ from those of non-Hispanic white (NHW) children. We evaluated the phenotypic differences at presentation between AA and NHW children enrolled in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry, and replicated the findings in a JIA cohort from a large center in the southeastern United States. METHODS: Children with JIA enrolled in the multicenter CARRA Registry and from Emory University formed the study and replication cohorts. Phenotypic data on non-Hispanic AA children were compared with NHW children with JIA using the chi-square test, Fisher's exact test, and the Wilcoxon signed-rank test. RESULTS: In all, 4177 NHW and 292 AA JIA cases from the CARRA Registry and 212 NHW and 71 AA cases from Emory were analyzed. AA subjects more often had rheumatoid factor (RF)-positive polyarthritis in both the CARRA (13.4% vs 4.7%, p = 5.3 × 10(-7)) and the Emory (26.8% vs 6.1%, p = 1.1 × 10(-5)) cohorts. AA children had positive tests for RF and cyclic citrullinated peptide antibodies (CCP) more frequently, but oligoarticular or early onset antinuclear antibody (ANA)-positive JIA less frequently in both cohorts. AA children were older at onset in both cohorts and this difference persisted after excluding RF-positive polyarthritis in the CARRA Registry (median age 8.5 vs 5.0 yrs, p = 1.4 × 10(-8)). CONCLUSION: Compared with NHW children, AA children with JIA are more likely to have RF/CCP-positive polyarthritis, are older at disease onset, and less likely to have oligoarticular or ANA-positive, early-onset JIA, suggesting that the JIA phenotype is different in 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 | 0.001 | 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 | 0.000 | 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) | 0.001 | 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".