Treatment of vitiligo with topical ruxolitinib: a narrative review
Bibliographic record
Abstract
Vitiligo is a chronic autoimmune disorder characterized by the selective destruction of melanocytes, leading to depigmented patches of skin. Whilst its pathogenesis is not fully understood, genetic predisposition, environmental triggers, oxidative stress, metabolic dysfunction and impaired cell adhesion are all implicated. Vitiligo occurs in two primary forms - non-segmental and segmental - and affects approximately 0.5-2% of the global population. Beyond its physical manifestations, vitiligo imposes a significant psychosocial burden on patients. Current treatments include topical corticosteroids, calcineurin inhibitors, systemic immunosuppressants and narrowband UVB phototherapy. More recently, Janus kinase (JAK) inhibitors have emerged as promising targeted therapies. Topical ruxolitinib 1.5% cream has been approved by both the FDA and EMA for the treatment of non-segmental vitiligo in adolescents and adults, following its demonstrated efficacy and favourable tolerability in clinical trials. Although some risks, such as infection, malignancy, major adverse cardiovascular events and thrombosis, have been raised due to class-wide JAK inhibition concerns, these events appear to be rare with topical use, as no systemic drug accumulation has been reported. Given its safe and therapeutic profile, ruxolitinib is an effective targeted therapy for non-segmental vitiligo. This narrative study aims to review and synthesize the current evidence on the safety, efficacy and therapeutic impact of topical ruxolitinib cream in vitiligo.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".