Off-label Studies on the Use of Ruxolitinib in Dermatology
Bibliographic record
Abstract
PURPOSE: Ruxolitinib (Jakafi) is a Janus kinase 1 and 2 small molecule inhibitor that the Food and Drug Administration approved for myelofibrosis and polycythemia vera. It has been expanded to off-label treatment for a variety of dermatologic conditions, with several clinical trials ongoing. A review of available studies and cases of off-label uses was performed to guide clinicians seeking evidence on the efficacy of this Janus kinase inhibitor for dermatologic disorders. MATERIALS AND METHODS: PubMed/MEDLINE, EMBASE, Scopus, and ClinicalTrials.gov databases were searched with the term "ruxolitinib," and results were manually reviewed to identify published data on off-label uses of ruxolitinib. Studies included are structured by quality of evidence available. RESULTS: Ruxolitinib may have utility in the treatment of atopic dermatitis, psoriasis, and vitiligo, with data from open-label and randomized trials supporting efficacy of topical formulations. Evidence of utility for alopecia areata is mixed and differs depending on topical versus oral form. Evidence for numerous other conditions is available through case reports and case series. CONCLUSIONS: There is growing evidence supporting potential off-label use of oral and topical ruxolitinib for a wide range of skin conditions. There are several ongoing investigations of ruxolitinib use in dermatology that will undoubtedly better define its efficacy and appropriate use in dermatology.
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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 | 0.023 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".