COVID‐19 related masks increase severity of both acne (maskne) and rosacea (mask rosacea): Multi‐center, real‐life, telemedical, and observational prospective study
Bibliographic record
Abstract
Masks are essential for COVID-19 prevention, but recently they were suggested to modify cutaneous facial microenvironment and trigger facial dermatoses. To evaluate mask-related rosacea and acne (maskne) in untreated patients during lockdown. In this multi-center, real-life, observational prospective study, we enrolled stable, untreated acne and rosacea patients that wore masks during lockdown at least 6 h/day. They underwent two teledermatological consultations, at the baseline and after 6 weeks. Clinical, pharmacological, and psychological data were recorded. A total 66 patients, 30 (median age: 34.0 [30.25-29.75] yoa) with acne and 36 patients (median age: 48 [43-54] years) with rosacea, were enrolled in this study. After 6 weeks of mask and quarantine, patients with acne displayed an increased Global Acne Grading Scale (GAGS) score in mask-related areas (P < .0001). Likewise, after 6 weeks of mask and quarantine, patients with rosacea displayed a worsen in both physican (P < .0001) and patient (P < .0001) reported outcomes. Remarkably, patients reported also a statistically significant decrease in their quality of life (P < .0001). Masks appear to trigger both acne and rosacea flares. Additional studies are needed to generate evidence and inform clinical decision-making.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".