Effectiveness of tumor necrosis factor inhibitors in children with enthesitis-related arthritis: a single-center retrospective analysis
Bibliographic record
Abstract
Objective In children with enthesitis-related arthritis (ERA), the hip and sacroiliac joint function might be impaired if not properly treated. We sought to evaluate the effectiveness of anti-tumor necrosis factor-α (TNF-α) therapy using the inflammatory indicators, Juvenile Arthritis Disease Activity Score 27 (JADAS27) and magnetic resonance imaging (MRI). Methods We conducted a single-center retrospective study of 134 patients with ERA. We evaluated the effect of anti-TNF therapy on the inflammatory indicators, active joint count, MRI quantitative score, and JADAS27 over 18 months. We used the Spondyloarthritis Research Consortium of Canada (SPARCC) and the Hip Inflammation MRI Scoring System (HIMRISS) scoring systems for hip and sacroiliac joints scoring. Results The average age of onset of children with ERA was 11.62 ± 1.95 years, and they were treated with disease-modifying antirheumatic drugs (DMARDs) combined with biologics ( n = 87, 64.93%). There were no differences in HLA-B27 positivity between the biologics and non-biologics treatment groups [66 (49.25%) vs . 68 (50.75%), P > 0.05]. Children who received anti-TNF (71 received etanercept, 13 adalimumab, 2 golimumab, and 1 infliximab) therapy improved significantly. Children with ERA used DMARDs and biologics at baseline (Group A) were followed up to 18 months, and their active joint count (4.29 ± 1.99 vs. 0.76 ± 1.33, P = 0.000), JADAS27 (13.70 ± 4.80 vs. 4.53 ± 4.52, P = 0.000) and MRI quantitative scores ( P = 0.001) were significantly lower than those at baseline. Some of the patients ( n = 13, 9.70%) were treated with DMARDs at the onset of the disease, but did not show significant improvement (Group B). After 6−18 months of switching to anti-TNF therapy, related indicators of the children were significantly lower than at baseline and 1 month ( P < 0.013). At 18 months, a total of 33 patients ( n = 74, 44.59%) in Group A and 7 ( n = 13, 53.85%) in Group B reached inactive state. Conclusion Eighteen months after diagnosis, anti-TNF therapy was found to be effective in children diagnosed with ERA. MRI is important for the early diagnosis of juvenile idiopathic arthritis. TNF-α inhibitors can significantly improve the clinical manifestations of sacroiliac joint and hip involvement in patients with ERA. Overall, the real-world study provides more evidence for precision diagnosis and treatment for other hospitals, families and patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| 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".