Joint Pain and Inflammatory Arthropathy after Dupilumab Use in Atopic Dermatitis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract: Dupilumab has emerged as an important treatment for inadequately controlled moderate-to-severe atopic dermatitis (AD). However, joint pain after dupilumab use has prompted the inclusion of arthralgia in the Summary of Product Characteristics of dupilumab. In this article we reviewed arthritis and inflammatory arthropathy as an adverse effect of dupilumab treatment for AD. A comprehensive search was performed for randomized controlled trials, observational studies, case series, and case reports analyzing joint pain prevalence among AD patients treated with dupilumab. This study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 checklist and quality was assessed via the Newcastle–Ottawa Quality Assessment Scale (NOS). Forty-three studies were included in the systematic review, and 34 studies (15,101 patients) were included in the meta-analysis. Among the 34 studies, the pooled prevalence of joint pain among dupilumab patients was 1.74% (95% confidence interval [CI]: 1.12–2.68). Among the eight studies that reported inflammatory arthropathy prevalence among dupilumab patients, the pooled prevalence of inflammatory arthropathy among dupilumab patients was 2.62% (95% CI: 1.44–4.70). Among the eight studies with a control group, the overall relative risk of developing joint pain after dupilumab use for AD was not significantly higher at 0.68 (95% CI: 0.29–1.58, P = 0.31), compared to the control group. Although our findings found that there is insufficient evidence to suggest that dupilumab use was associated with joint pain, the prevalence of joint pain observed among patients treated with dupilumab may warrant proactive surveillance in clinical practice.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.041 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".