Disease-modifying Antirheumatic Drug Use in the Treatment of Juvenile Idiopathic Arthritis: A Cross-sectional Analysis of the CARRA Registry
Bibliographic record
Abstract
OBJECTIVE: To characterize disease-modifying antirheumatic drug (DMARD) use for children with juvenile idiopathic arthritis (JIA) in the United States and to determine patient factors associated with medication use. METHODS: We analyzed cross-sectional baseline enrollment data from the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry from May 2010 through May 2011 for children with JIA. Current and prior medication use was included. We used parsimonious backward stepwise logistic regression models to calculate OR to estimate associations between clinical patient factors and medication use. RESULTS: We identified 2748 children with JIA with a median disease duration of 3.9 years from 51 US clinical sites. Overall, 2023 (74%) had ever received a nonbiologic DMARD and 1246 (45%) had ever received a biologic DMARD. Among children without systemic arthritis, methotrexate use was most strongly associated with uveitis (OR 5.2, 95% CI 3.6-7.6), anticitrullinated protein antibodies (OR 4.5, 95% CI 1.7-12), and extended oligoarthritis (OR 4.1, 95% CI 2.5-6.6). Among children without systemic arthritis, biologic DMARD use was most strongly associated with rheumatoid factor (RF)-positive polyarthritis (OR 4.3, 95% CI 2.9-6.6), psoriatic arthritis (PsA; OR 3.0, 95% CI 2.0-4.4), and uveitis (OR 2.8, 95% CI 2.1-3.7). Among children with systemic arthritis, 160 (65%) ever received a biologic DMARD; tumor necrosis factor inhibitor use was associated with polyarthritis (OR 2.5, 95% CI 3.8-16), while interleukin 1 inhibitor use was not. CONCLUSION: About three-quarters of all children with JIA in the CARRA Registry received nonbiologic DMARD. Nearly one-half received biologic DMARD, and their use was strongly associated with RF-positive polyarthritis, PsA, uveitis, and systemic arthritis.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".