Catch‐Up Growth During Tocilizumab Therapy for Systemic Juvenile Idiopathic Arthritis: Results From a Phase III Trial
Bibliographic record
Abstract
OBJECTIVE: To investigate the impact of tocilizumab treatment on growth and growth-related laboratory parameters in patients with systemic juvenile idiopathic arthritis (JIA) enrolled in a phase III clinical trial. METHODS: Patients with systemic JIA ages 2-17 years (n = 112) received tocilizumab in a 12-week, randomized, placebo-controlled period and a long-term open-label extension. Height velocity and standard deviation (SD) score; levels of insulin-like growth factor 1 (IGF-1), osteocalcin (OC), and C-telopeptide of type I collagen (CTX-I); and Juvenile Arthritis Disease Activity Score in 71 joints (JADAS-71) were measured in a post hoc analysis of 83 patients who never received growth hormone and did not reach Tanner stage 5 by the end of the first year of treatment. RESULTS: Patients had stunted growth at baseline (mean height SD score -2.2). During tocilizumab treatment, males (73%) and females (83%) experienced above-normal mean height velocities of 6.6 cm/year (P < 0.0001 versus World Health Organization norms). Mean height SD score increases during year 1 (0.29) and year 2 (0.31) were significant (both P < 0.0001). The mean SD score for IGF-1 levels increased significantly (-0.2 for year 1 and -0.1 for year 2 versus -1.0 at baseline; both P < 0.0001). Mean OC and CTX-I levels (both P < 0.0001) and the OC:CTX-I ratio (P = 0.014) significantly increased from baseline to year 2. In multiple regression analysis, first-year height velocity had a significant inverse relationship to JADAS-71 at year 1, age, mean glucocorticoid dosage during the year, and height SD score at baseline. CONCLUSION: Our findings indicate that during treatment with tocilizumab, patients with systemic JIA experience significant catch-up growth, normalization of IGF-1 levels, and bone balance improvement favoring bone formation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".