Facial atopic dermatitis may be exacerbated by masks: insights from a multicenter, teledermatology, prospective study during COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Patients with atopic dermatitis (AD) display a defective skin barrier, consequently they may experience inflammatory flares with different exposures, including masks. Actually, beside scattering case reports, no study focused on the possible AD flaring due to masks. METHODS: In this multicenter prospective study AD patients with facial manifestation were followed with teledermatology and evaluated by two board-certified dermatologists at the baseline (T0) and after 1 month (T1) in which patients started to wear masks >6 hours per day. Demographics and clinical parameters, included and not limited to Eczema Area and Severity Index (EASI) and Dermatology Life Quality Index (DLQI), were carefully collected and analyzed. RESULTS: We enrolled 57 AD patients (M/F 28/29, 33.91±12.26 years old) that wore surgical masks (38 [66.7%]), community masks (11 [19.3%] and N95 (8 [14.0%]). Both DLQI and EASI increase during the time period (P<0.0001). DLQI variation was not influenced by age, BMI, and gender, mask type used and AD therapy (P=0.99), whilst EASI variation was significantly influenced by BMI, gender, and therapy (P=0.004). CONCLUSIONS: Mask wearing may prove detrimental to patients with atopic eczema and the same may not necessarily be the case for asthma patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".