The Systemic Juvenile Idiopathic Arthritis Cohort of the Childhood Arthritis and Rheumatology Research Alliance Registry: 2010–2013
Bibliographic record
Abstract
OBJECTIVE: We aimed to identify the (1) demographic/clinical characteristics, (2) medication usage trends, (3) variables associated with worse disease activity, and (4) characteristics of patients with persistent chronic arthritis in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Legacy Registry's systemic juvenile idiopathic arthritis (sJIA) cohort. METHODS: Demographics, disease activity measures, and medications at enrollment of patients with sJIA in the CARRA Registry were analyzed using descriptive statistics. Multivariate analyses were conducted to identify associations with increased disease activity. Medication usage frequencies were calculated by year. RESULTS: There were 528 patients with sJIA enrolled in the registry (2010-2013). There were 435 patients who had a complete dataset; of these, 372 met the International League of Associations for Rheumatology criteria and were included in the analysis. At enrollment, median disease duration and joint count were 3.7 years and 0, respectively; 16.4% had a rash and 6.7% had a fever. Twenty-six percent were taking interleukin 1 (IL-1) inhibitors and 29% glucocorticoids. Disease-modifying antirheumatic drugs and tumor necrosis factor inhibitors use decreased, while IL-6 inhibitor use increased between 2010 and 2013. African American patients had worse joint counts (p = 0.003), functional status (p = 0.01), and physician's global assessment (p = 0.008). Of the 255 subjects with > 2 years of disease duration, 56% had no arthritis or systemic symptoms, while 32% had persistent arthritis only. CONCLUSION: Most patients in the largest sJIA cohort reported to date had low disease activity. Practice patterns for choice of biologic agents appeared to change over the study period. Nearly one-third had persistent arthritis without systemic symptoms > 2 years after onset. African Americans were associated with worse disease activity. Strategies are needed to improve outcomes in subgroups with poor prognosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".