Masks trigger facial seborrheic dermatitis and psoriasis: evidence from a multicenter, case-control study during COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Wearing masks is an optimal preventive strategy during COVID-19 pandemic, but it may increase facial sebum production. However, few case reports have described seborrheic dermatitis (SeBD) and psoriasis (PsO) flares due to masks. Hence, we conducted a multicenter study to clarify the possibility of increased SeBD and PsO flares in association with mask wearing during the COVID-19 pandemic. METHODS: This multicenter study enrolled patients with a diagnosis of facial SeBD and PsO. All dermatological consultations were conducted in teledermatology at baseline (T0) and after 1 month (T1) Of >6 hours/day wearing mask. PsO patients were assessed using PsO Area and Severity Index (PASI) and self-administered PASI (SAPASI), whilst SeBD patients with symptom scale of seborrheic dermatitis' (SSSD) and seborrheic dermatitis area and severity index (SEDASI). RESULTS: A total of 33 (20 males, 13 females, average age 43.61±9.86) patients with PsO and 33 (20 males, 13 females, average age 44.00±8.58) with SeBD were enrolled. After 1 month, PsO patients displayed higher values of both PASI and SAPASI (P<0.0001), while SeBD patients experienced a flare, as testified by the increment of both SSSD and SEDASI (P<0.0001). Mask type did not seem to influence the flare severity. CONCLUSIONS: Masks remain an optimal preventive strategy during COVID-19 pandemic, but patients with PsO and SeBD may experience facial flares. Thus, therapeutic approach should be more aggressive in these groups of patients to counteract the triggering effect of masks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".