Real-world data reveals the complexity of disease modifying anti-rheumatic drug treatment patterns in juvenile idiopathic arthritis: an observational study
Bibliographic record
Abstract
OBJECTIVE: Pharmacological treatment is a cornerstone of care for children with juvenile idiopathic arthritis (JIA). The objective of this study is to evaluate prescription patterns of conventional and biologic disease modifying anti-rheumatic drugs (c-DMARDs and b-DMARDs) for patients with JIA. METHODS: We conducted a retrospective cohort study of children diagnosed with JIA at a rheumatology pediatric clinic. Eligibility criteria were defined as children and youth newly diagnosed with enthesis-related arthritis, polyarticular, or oligoarticular JIA between 2011 and 2019, with at least one year of observation. Data on c-DMARDs and b-DMARDs prescriptions were obtained from electronic medical charts. We used descriptive statistics, Kaplan-Meier survival methods, and Sankey diagrams to describe treatment prescription patterns. RESULTS: A total of 325 patients with JIA were included, with a median observation time of 3.7 years. The most frequently prescribed c-DMARD and b-DMARD were methotrexate and etanercept, respectively. Within the first year of rheumatology care, 62% and 21% of patients had a c-DMARD and a b-DMARD prescribed, respectively. These proportions varied greatly by JIA subtype. Among the 147 (147/325, 45%) patients that had at least one b-DMARD prescribed, 24% were prescribed a second, and 7% a third-line of b-DMARD. A total of 112 unique treatment sequences were observed, with c-DMARD monotherapy followed by the addition of either a b-DMARD (56%) or another c-DMARD (30%) being the two most prevalent patterns in this cohort. CONCLUSION: We observed a variety of treatment trajectories, with many patients experiencing multiple treatment lines, illustrating the complexity of the overall JIA treatment path.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".