Subcutaneous golimumab for children with active polyarticular-course juvenile idiopathic arthritis: results of a multicentre, double-blind, randomised-withdrawal trial
Bibliographic record
Abstract
OBJECTIVE: This report aims to determine the safety, pharmacokinetics (PK) and efficacy of subcutaneous golimumab in active polyarticular-course juvenile idiopathic arthritis (polyJIA). METHODS: of body surface area; maximum: 50 mg/dose) every 4 weeks together with weekly methotrexate during Part 1 (weeks 0-16). Patients with at least 30% improvement per American College of Rheumatology Criteria for JIA (JIA ACR30) in Part 1 entered the double-blinded Part 2 (weeks 16-48) after 1:1 randomisation to continue golimumab or start placebo. In Part 3, golimumab was continued or could be restarted as in Part 1. The primary outcome was JIA flares in Part 2; secondary outcomes included JIA ACR50/70/90 responses, clinical remission, PK and safety. RESULTS: Among 173 patients with polyJIA enrolled, 89.0% (154/173) had a JIA ACR30 response and 79.2%/65.9%/36.4% demonstrated JIA ACR50/70/90 responses in Part 1. At week 48, the primary endpoint was not met as treatment groups had comparable JIA flare rates (golimumab vs placebo: 32/78=41% vs 36/76=47%; p=0.41), and rates of clinical remission were comparable (golimumab vs placebo: 10/78=12.8% vs 9/76=11.8%). Adverse event and serious adverse event rates were similar in the treatment groups during Part 2. Injection site reactions occurred with <1% of all injections. PK analysis confirmed adequate golimumab dosing for polyJIA. CONCLUSION: Although the primary endpoint was not met, golimumab resulted in rapid, clinically meaningful, improvement in children with active polyJIA. Golimumab was well tolerated, and no unexpected safety events occurred. CLINICAL TRIAL REGISTRATION: NCT01230827; Results.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".