Canadian Consensus Guidelines for the Management of Vitiligo
Bibliographic record
Abstract
INTRODUCTION: Vitiligo remains a highly burdensome disease associated with significant autoimmune and psychosocial comorbidities. Although the therapeutic landscape has long been dominated by off-label therapy, new treatments are emerging. Limited guidance on how to safely and effectively utilize available therapies poses challenges for healthcare providers. Herein, we provide generally accepted principles, consensus recommendations, and a treatment algorithm for the management of vitiligo, as developed by a panel of ten Canadian dermatologists with expertise in managing vitiligo. METHODS: The three-phase process consisted of identifying themes and research questions; conducting a systematic literature review; and discussing/voting on generally accepted principles, consensus statements, and a treatment algorithm using an iterative consensus process. RESULTS: Experts agreed to 27 generally accepted principles, ten consensus statements, and a treatment algorithm. Education about vitiligo pathogenesis and repigmentation biology can help patients, caregivers, and healthcare providers set realistic expectations for treatment. Treatment should focus on repigmentation or stabilizing progression, rather than on depigmentation. Topical therapies include topical corticosteroids, topical calcineurin inhibitors, and the topical Janus kinase inhibitor ruxolitinib cream. Phototherapy, such as narrow-band ultraviolet B and excimer laser/lamp, can be used as monotherapy or in combination with other treatments. Off-label systemic therapies may be appropriate for patients with unstable or rapidly progressing disease. Surgical therapy may be suitable for patients with localized or stable recalcitrant disease. Maintenance therapy may help mitigate the risk of disease relapse. CONCLUSION: Improved clarity around the benefits, risks, and limitations of available therapies has supported the development of robust guidelines and a treatment algorithm for vitiligo. Disease stabilization and repigmentation are goals that can largely be achieved, particularly when patients share a mutual understanding of vitiligo and its treatment options. A Graphical Abstract is available for this article.
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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 | 0.034 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".