Attainment of Inactive Disease Status Following Initiation of TNF-α Inhibitor Therapy for Juvenile Idiopathic Arthritis: Enthesitis-related Arthritis Predicts Persistent Active Disease
Bibliographic record
Abstract
OBJECTIVE: To analyze the attainment of inactive disease following initiation of tumor necrosis factor-α (TNF-α) inhibitors in a heterogeneous cohort of children with juvenile idiopathic arthritis (JIA). METHODS: We performed retrospective chart review of all children with JIA at 1 academic center who had started TNF-α inhibitor therapy. We retrospectively determined inactive disease status according to the 2004 criteria of Wallace, et al. We evaluated inactive disease status at 1 year after initiation of TNF-α inhibitor and attainment of inactive disease at any point during the study period. Predictors of inactive disease were determined using univariate analyses and multivariable logistic regression models. RESULTS: A total of 125 patients started TNF-α inhibitors, and 88 patients had data available for the 1-year followup visit. Many patients (49%) started TNF-α inhibitors within 6 months of the diagnosis of JIA. Diverse JIA phenotypes were represented: at baseline, 29% of all patients had active enthesitis and only 23% had active polyarthritis. At the 1-year followup, 36 of 88 (41%) patients had inactive disease. Overall, 67 of 125 (54%) patients ever attained inactive disease status during the study period. In multivariable models, enthesitis-related arthritis (ERA) and higher Childhood Health Assessment Questionnaire (CHAQ) scores at baseline were independently associated with failure to later attain inactive disease status. CONCLUSION: Treatment with TNF-α inhibitors appears to be less effective for attaining inactive disease status in patients with ERA or higher baseline CHAQ scores. Further studies are needed regarding the clinical effectiveness of TNF-α inhibitor therapy and the optimal treatment of ERA.
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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) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".