Masks use and facial dermatitis during COVID-19 outbreak: is there a difference between CE and non-CE approved masks? Multi-center, real-life data from a large Italian cohort
Bibliographic record
Abstract
BACKGROUND: During the recent COVID-19 outbreak, masks became mandatory and shortages frequent, therefore the prevalence of non-CE (European Conformity Mark) approved masks increased in the general population. We aimed to quantify the prevalence of mask-related cutaneous side effects and the differences between CE and non-CE approved masks. METHODS: In this multicenter prospective observational study conducted from March 20, 2020 to May 12, 2020(during and after quarantine), patients attending emergency departments for a dermatological consult were clinically assessed and their masks were inspected to detect CE marks and UNI (Italian National Unification Entity) norms. Patients with history of facial dermatoses or under current treatment for facial dermatoses were excluded. RESULTS: We enrolled 412 patients (318 during quarantine and 94 after quarantine). CE-approved masks were observed 52.8% vs. 24.5%, whilst subsets of non-CE approved masks were 9.7% vs. 14.9% (Personal protective equipment (PPE)-masks), 16.4% vs. 12.8% (surgical masks [SM]), and 21.1% vs. 47.9%(non-PPE) and (non-SM masks), respectively during and after quarantine. Remarkably, non-CE-approved masks resulted in patients displaying a statistically significant higher incidence of facial dermatoses and irritant contact dermatitis compared to CE-approved masks, and these differences were mainly driven by non-PPE non-SM masks. Comparing quarantine and after quarantine periods, no statistically significant differences were found for CE-approved masks, whilst differences were detected in non-CE-approved masks regarding incidence of facial dermatoses (P<0.0001)and irritant contact dermatitis (P=0.0041). CONCLUSIONS: Masks are essential to prevent COVID-19 but at the same time higher awareness regarding mask specifications should be promoted in the general population. Non-PPE and non-SM masks should undergo more rigorous testing to prevent the occurrence of cutaneous side effects and future patients' lawsuit damages.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".