Retrospective analysis of infliximab and adalimumab treatment in a large cohort of juvenile dermatomyositis patients
Bibliographic record
Abstract
Abstract Background Anti-TNF treatment may be useful for the treatment of patients with refractory juvenile dermatomyositis (JDM). The aim of this study was to describe the use of infliximab and adalimumab therapy in juvenile dermatomyositis as an adjunctive treatment. Methods Sixty children recruited to the UK JDM Cohort and Biomarker Study that had received at least 3 months of anti-TNF treatment (infliximab or adalimumab) were studied. Childhood Myositis Assessment Scale (CMAS), Manual Muscle Testing (MMT8) and physician’s global assessment (PGA) were recorded. Skin disease was assessed using the modified skin disease activity score (DAS). Data were analysed using Friedman’s test for repeated measures analysis of variance. Results Compared to baseline, there were improvements at 6 and 12 months in skin disease ( χ 2 (2) = 15.52, p = 0.00043), global disease ( χ 2 (2) = 8.14, p = 0.017) and muscle disease (CMAS χ 2 (2) = 17.02, p = 0.0002 and MMT χ 2 (2) = 10.56, p = 0.005) in infliximab patients. For patients who switched from infliximab to adalimumab, there was improvement in global disease activity ( χ 2 (2) = 6.73, p = 0.03), and trends towards improvement in CMAS, MMT8 and modified DAS. The median initial prednisolone dose was 6 [0–10] mg, and final was 2.5 [0–7.5] mg ( p < 0.0001). Fifty-four per cent of patients had a reduction in the number and/or size of calcinosis lesions. Twenty-five per cent switched their anti-TNF treatment from infliximab to adalimumab. 66.7%of the switches were to improve disease control, 26.7% due to adverse events and 6.6% due to patient preference. A total of 13.9 adverse reactions occurred in 100 patient-years, of which 5.7 were considered serious. Conclusion Reductions in muscle and skin disease, including calcinosis, were seen following treatment with infliximab and adalimumab.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".