Evaluation of Health Disparities in Outcomes of Patients With Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Juvenile idiopathic arthritis (JIA) is complicated by morbidity, with suboptimal rates of prolonged remission, decreased health-related quality of life, and functional limitations. The Pediatric Rheumatology Care and Outcomes Improvement Network (PR-COIN), a North American learning health network, has a centralized registry of patients with JIA to track quality measures. We assessed for health disparities in our collaborative JIA population by evaluating our performance on disease activity outcomes, overall well-being, and pain by race and ethnicity, age, sex, and JIA subtype. METHODS: A cross-sectional analysis of patients in the PR-COIN registry was conducted to estimate the association between race and ethnicity groups and outcomes including physician global assessment of disease activity (PGA), patient/parent global assessment of overall well-being (PtGA), active joint count, 10-joint clinical Juvenile Arthritis Disease Activity Score (cJADAS10), arthritis-related pain intensity score, and morning stiffness duration. RESULTS: Data from 9601 patients were analyzed. Current age was positively and significantly associated with higher scores of pain intensity, PGA, PtGA, and cJADAS10. Non-Hispanic Black patients had statistically higher cJADAS10 scores compared to non-Hispanic White patients, in addition to statistically higher pain intensity scores, and PGA and PtGA scores. Female patients had statistically higher scores compared to male patients for all outcome variables assessed. CONCLUSION: We found disparities in outcomes of patients with JIA related to race and ethnicity, sex, and age. This information is imperative to drive further improvement efforts and understand possible causes of these differences to close disparity gaps and improve outcomes for all patients with JIA.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".