Vitiligo in the Digital Spotlight: Retrospective Longitudinal Study in Germany
Bibliographic record
Abstract
Background: Vitiligo is a chronic skin disease with a global prevalence of approximately 1% to 2%, characterized by depigmented macules. Little is known about the public interest and medical needs of patients with vitiligo in Germany. However, understanding this is critical for a patient-centered holistic therapeutic management of the disease. Objective: This study aimed to analyze vitiligo-related web search behavior across Germany as a proxy for public awareness. A retrospective longitudinal study was conducted using Google Ads Keyword Planner to collect monthly search volume data for vitiligo-related terms from October 2019 to May 2023. Methods: Keywords were identified in the 7 most spoken languages in Germany (German, Turkish, English, Arabic, Russian, and Polish). Seasonal and regional variations were analyzed, along with correlations with population density, dermatologist availability, and weather patterns. Results: In total, 7,764,080 vitiligo-related searches were recorded. Most searches (n=5,808,360, 74.81%) addressed general information. Search volume peaked during the summer months and correlated positively with temperature and sunshine hours (P<.001). Notable regional differences were observed, with the highest search rates in Hamburg, Berlin, and Bremen. Rural areas showed higher search volume per 100,000 inhabitants than urban areas. Conclusions: The findings suggest a strong public interest in vitiligo, particularly during periods of increased skin exposure. The high demand for treatment-related information further reflects the need for accessible, effective care. Web search behavior can offer real-time insights into public awareness and unmet needs, supporting earlier disease recognition, stigma reduction, and targeted educational strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".