Recent Trends in Medication Usage for the Treatment of Juvenile Idiopathic Arthritis and the Influence of Tumor Necrosis Factor Inhibitors
Bibliographic record
Abstract
OBJECTIVE: Using administrative data from a large commercial US health insurer, we investigated temporal trends in medication use among children diagnosed with juvenile idiopathic arthritis (JIA). METHODS: Children with ≥ 1 physician diagnosis code for JIA in the calendar years 2005 through 2012 were included. Use of tumor necrosis factor inhibitors (TNFi), methotrexate (MTX), nonsteroidal antiinflammatory drugs (NSAID), and oral glucocorticoids (GC) was determined. Temporal changes in medication usage were evaluated with the Cochran-Armitage test for trend. We used paired t-tests to evaluate the use of NSAID and GC in the 6 months before and after new TNFi use. RESULTS: We identified 4261 unique individuals with JIA. The proportion of patients receiving TNFi increased from 8.7% in 2005 to 22.4% in 2012 (p < 0.0001). MTX use increased from 18.4% to 23.2% (p = 0.02). NSAID use decreased from 49% to 40% (p = 0.02). GC use was relatively unchanged. Following new TNFi use, the mean number of NSAID prescriptions (among prevalent users) decreased from 2.8 to 2.0 (p < 0.0001), and the mean daily GC dose (among prevalent users) decreased from 7.3 mg/day to 3.9 mg/day (p < 0.0001). Many new TNFi users (57%) had not used MTX in the previous 6 months, and only 37% had any concurrent MTX use in the 6 months following new TNFi use. CONCLUSION: TNFi use in the treatment of JIA increased 2- to 3-fold over the last 8 years. New TNFi use was associated with decreased NSAID and GC use. TNFi may be replacing, rather than complementing, MTX in the treatment of many patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".