Efficacy and safety of topical JAK inhibitors in the treatment of atopic dermatitis in paediatrics and adults: A systematic review
Bibliographic record
Abstract
Atopic dermatitis (AD) is the most common skin inflammatory disease. Dysregulation of innate and adaptive immune systems plays a major role in the pathophysiology of AD. JAKi (Janus Kinase Inhibitors) reduce the production of pro-inflammatory cytokines and represent a promising novel treatment for AD. To assess and summarize the overall efficacy and safety of topial JAKi in the treatment of AD in adults and pediatrics, a broad search was performed on Ovid Medline, Ovid Embase, Cochrane Library, Web of Sciences, Scopus, CINAHL and Google Scholar until 14 June 2022. After screening, 19 studies remained for the final review. The current systematic review was conducted according to PRISMA, and the protocol was registered in PROSPERO (ID #CRD42022303321). Topical delgocitinib, tofacitinib, ruxolitinib, cerdulatinib and ifidancitinib are effective in treating AD and significantly improve EASI, IGA, pruritus-NRS score and some other indexes in adults. Moreover, topical delgocitinib was observed to have a great efficacy in the treatment of AD in paediatrics. All topical JAKi showed minimal risk of mild-to-moderate adverse effects. Available topical JAKi are effective and safe modalities in treating AD. Nevertheless, further studies with longer duration and head-to-head comparative trials are necessary to find the best option with the least adverse effects.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".