Treatment of Atopic Dermatitis Using JAK Inhibitors: A Systematic Review
Bibliographic record
Abstract
Background: The advent of JAK inhibitors (JAKi) has significantly modernised the treatment of atopic dermatitis (AD), offering a novel approach to treating this recalcitrant dermatological condition. Although topical treatment is shown to be effective, oral formulations are yet to be widely utilised in the treatment of AD. Objectives: To review the efficacy, safety, and tolerability of JAKi in the treatment of AD. Methods: A PRISMA systematic review of several databases was conducted: Cochrane Skin Specialised Register, Cochrane Central Register of Controlled Trials, Ovid Medline and Embase, LILACS, and Global Resource of EczemA Trials. Five clinical trial archives were also consulted. The following resources were manually searched: conference proceedings of the American Academy of Dermatology (AAD), FDA.gov, the European Medicines Agency (EMA), and Epistemonikos. Results: Of the 34 articles meeting inclusion criteria, 6 were chosen for final qualitative review. A total of 827 patients were pooled from 5 randomised controlled trials and 1 cohort study. Improvements in objective and subjective scoring indices were observed in patients receiving topical or oral JAKi. Overall safety and tolerability were satisfactory in JAKi treatment. Limitations: Due to the scarcity of randomised controlled trials and the small sample sets in the studies, a meta-analysis was not conducted. Conclusions: Preliminary investigations show promising results for patients with AD treated with oral or topical JAKi. However, existing gaps should be addressed with more extensive and long-term trials before JAKi become a standard treatment for AD.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".