The Use of Janus Kinase Inhibitors in Vitiligo: A Review of the Literature
Bibliographic record
Abstract
Vitiligo is a common acquired depigmenting disorder characterized by the development of white macules and patches due to the loss of melanocytes. Patients with vitiligo can be stigmatized by society, making the disease a source of psychological stress that can considerably affect quality of life. The goal of vitiligo treatment is to obtain skin repigmentation in the majority of cases, and less commonly to depigment the remaining normal skin. There is no consistent, long-term, durable therapy for vitiligo for all patients, highlighting the unmet need for new safe and effective therapies to control this disease. Recently, JAK inhibitors have been explored as a promising novel treatment option in vitiligo. The JAK and signal transducers and activators of transcription (STAT) pathway is an attractive therapeutic target because IFN-γ-dependent cytokines produced through this pathway have been implicated in the pathogenesis of disease. This literature review describes vitiligo pathophysiology, explains the usefulness of the JAK inhibitors for treatment, and summarizes published case reports, case series, and open-label studies. Research outlined here shows JAK inhibitors in patients with vitiligo have a favorable safety profile and effectively produce repigmentation of lesions, especially with concomitant ultraviolet exposure. Additional studies are required to confirm efficacy, establish safety, and investigate durability of repigmentation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".