Comparative Effectiveness of Tumor Necrosis Factor Agents and Disease-modifying Antirheumatic Therapy in Children with Enthesitis-related Arthritis: The First Year after Diagnosis
Bibliographic record
Abstract
OBJECTIVE: To characterize the effect of anti-tumor necrosis factor (TNF) therapy compared to conventional synthetic disease-modifying antirheumatic drugs (csDMARD) in children with enthesitis-related arthritis (ERA) over the first year after diagnosis. METHODS: We conducted a multicenter retrospective comparative effectiveness study of children diagnosed with ERA. We estimated the effect of anti-TNF therapy on clinical variables (active joint count, tender entheses count) and patient-reported pain and global assessment of disease activity over the first year after diagnosis using state-of-the-art comparative effectiveness analytic methods. RESULTS: During the study period, 217 patients newly diagnosed with ERA had a total of 965 clinic visits the first year after disease diagnosis. Children [median age 11.6 yrs, interquartile range 10-14] were treated with anti-TNF monotherapy (n = 33, 15.2%), csDMARD monotherapy (n = 73, 33.6%), or both (n = 52, 23.9%) in the first year after disease diagnosis. There was a statistically significant improvement in the primary outcome, active joint count, over time in children who received an anti-TNF drug versus those who did not (p = 0.03). Additionally, use of anti-TNF therapy versus no anti-TNF therapy was associated with less patient-reported pain (p < 0.01) and improved disease activity over time as assessed by the clinical Juvenile Arthritis Disease Activity Score (p < 0.01). The magnitude of estimated effect on clinical outcomes was uniformly greater, with the exception of tender entheses count, in children treated with an anti-TNF drug versus a csDMARD. CONCLUSION: During the first year after diagnosis, anti-TNF exposure was associated with benefits for several clinically meaningful outcomes in children with enthesitis-related arthritis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".