Clinical Remission in Patients with Systemic Juvenile Idiopathic Arthritis Treated with Anti-Tumor Necrosis Factor Agents
Bibliographic record
Abstract
OBJECTIVE: To assess the frequency of clinical remission in a cohort of patients with systemic juvenile idiopathic arthritis (JIA) who received continuous anti-tumor necrosis factor (TNF) therapy; and to identify potential predictors of remission. METHODS: Patients with systemic JIA who were treated with anti-TNF agents for > 6 months were studied. Demographic and nosologic variables recorded at the start of anti-TNF therapy were analyzed. Association between early variables and occurrence of remission was evaluated through Cox proportional hazard regression analysis. RESULTS: Forty-five patients were included (30 girls), median age 9 years (range 2-17 yrs), age at disease onset 5 years (range 0.5-15), disease duration 3 years (range 0.5-13). Twenty-one (47%) children showed systemic symptoms at the start of anti-TNF therapy. Patients received therapy for 24 months (range 6-88): 45 (100%) were given etanercept, 17 (38%) infliximab, and 5 (11%) adalimumab, in combination with methotrexate. Anti-TNF switching was performed in 22 (49%) children. Eleven (24%) met definition criteria for remission while taking etanercept (n = 8), infliximab (2), or adalimumab (1). Remission occurred following 26 (range 9-65) months of therapy. Flares occurred in 5 (45%) patients 2 to 14 months after remission was first recorded. Absence of systemic symptoms at the start of therapy and fulfillment of improvement criteria at Month 3 were associated with remission in univariate analysis; no variable showed any association in multivariate analysis. CONCLUSION: Twenty-four percent of patients with systemic JIA experienced remission with anti-TNF therapy, but only 13% experienced sustained benefit.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".