Patients’ perspective on current treatments and demand for novel treatments in vitiligo
Bibliographic record
Abstract
BACKGROUND: The treatment of non-segmental vitiligo (NSV) remains a challenge. Current treatments often achieve suboptimal clinical results. To improve these treatment results, several new therapies are being developed and investigated. There is, however, little evidence on the actual need for novel therapies. OBJECTIVE: To assess patients' perspective on current and novel therapies for vitiligo. METHODS: A prospective questionnaire study was conducted in a large cohort of vitiligo patients that consecutively visited the outpatient clinic of the Amsterdam University Medical Centre between April 2017 and January 2019. Patients were requested to fill in a digital questionnaire on patient characteristics, disease burden, quality of life, efficacy and satisfaction of current treatments and aspects regarding new treatments. RESULTS: A total of 325 vitiligo patients completed the questionnaire (60% response rate). Of the respondents, 94% believed that new and improved treatments are needed and 86% would be willing to participate in clinical trials investigating a new therapy. Sixty-nine per cent would agree on taking weekly injections if it led to effective treatment results. Of the patients that had received therapy before, 49% reported that the current treatments were not effective and 50% was not satisfied with the current treatments. Sixty-seven per cent of the patients experienced facial lesions as an extreme burden, whereas this was, 25%, 12% and 10% for lesions on the hands, trunk and feet, respectively. The emotional burden score was significantly higher in dark skin types compared with light skin types (respectively, 8 vs 5, U P < 0.05). CONCLUSION: There is a substantial need for new vitiligo therapies. A considerable number of patients in our study are dissatisfied with current treatments and are emotionally burdened by the disease. Moreover, the vast majority demands novel treatments and is willing to participate in clinical trials.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".