Worldwide expert recommendations for the diagnosis and management of vitiligo: Position statement from the international Vitiligo Task Force—Part 2: Specific treatment recommendations
Bibliographic record
Abstract
BACKGROUND: The treatment of vitiligo can be challenging. Up-to-date agreed consensus recommendations on the use of topical and systemic therapies to facilitate the clinical management of vitiligo are currently lacking. OBJECTIVES: To develop internationally agreed-upon expert-based recommendations for the treatment of vitiligo. METHODS: In this consensus statement, a consortium of 42 international vitiligo experts and four patient representatives participated in different online and live meetings to develop a consensus management strategy for vitiligo. At least two vitiligo experts summarized the evidence for different topics included in the algorithms. A survey was then given to a core group of eight experts to resolve the remaining issues. Subsequently, the recommendations were finalized and validated based on further input from the entire group during two live meetings. RESULTS: The recommendations provided summarize the latest evidence regarding the use of topical therapies (steroids, calcineurin inhibitors and Jak-inhibitors) and systemic therapies, including steroids and other systemic immunomodulating or antioxidant agents. The different modalities of phototherapies (NB-UVB, photochemotherapy, excimer devices and home phototherapy), which are often combined with other therapies, are also summarized. Interventional approaches as well as depigmentation strategies are presented for specific indications. Finally, the status of innovative and targeted therapies under development is discussed. CONCLUSIONS: This international consensus statement culminated in expert-based clinical practice recommendations for the treatment of vitiligo. The development of new therapies is ongoing in vitiligo, and this will likely improve the future management of vitiligo, a disease that still has many unmet needs.
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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 | 0.046 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 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".