Effectiveness and Safety of High‐Dose Biologics in Juvenile Idiopathic Arthritis in the Childhood Arthritis and Rheumatology Research Alliance
Bibliographic record
Abstract
OBJECTIVE: To describe high-dose biologic use when treating juvenile idiopathic arthritis (JIA). METHODS: Patients with JIA enrolled in the Childhood Arthritis and Rheumatology Research Alliance Registry and treated with a biologic after enrollment were eligible. We described the frequency of high-dose biologic use and characteristics of patients receiving high-dose biologics. We used regression modeling to compare 6-month outcomes (using disease activity measures) between those who increased their biologic from standard to high dose (high-dose group) to those who initiated and remained on standard dosing (no-change group), and to those who switched biologic agents (biologic-switch group). We also compared serious adverse events (SAEs) between groups. RESULTS: A total of 5,352 patients with JIA were treated with biologics following enrollment; 1,080 (20%) had ever received a high-dose biologic. There were no significant differences in outcomes between the high-dose group and the biologic-switch group; both improved disease activity measures, including the clinical Juvenile Arthritis Disease Activity Score 10 (-3.53 and -3.95, respectively; P = 0.68). Although the SAE rates in the high-dose group and the biologic-switch group were numerically higher than the no-change group, the event rates were similar, and neither rate was significantly higher than in the no-change group (unadjusted incident rate ratio 2.5 [95% confidence interval (95% CI) 0.7-8.5] and 1.8 [95% CI 0.7-4.6], respectively). CONCLUSION: Dosing escalation appears to be a reasonable choice to improve disease control, but large, prospective, randomized studies evaluating specific biologic agents are needed.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".