Mask‐induced Koebner phenomenon and its clinical phenotypes: A multicenter, real‐life study focusing on 873 dermatological consultations during COVID‐19 pandemics
Bibliographic record
Abstract
During COVID-19 pandemic, wearing masks for prevention became mandatory but evidence suggest that is also detrimental for skin. Although facial dermatoses due to masks increase in both healthcare workers and general population, a pathogenetic hypothesis remains still elusive. We aimed to evaluate the prevalence of dermatological consultations due to Koebner triggered dermatoses In this prospective, multicenter, real life study carried out in Italy from March 11th to December 11th 2020 during COVID-19 pandemics, dermatological consultations (in-person and telemedicine) to study the prevalence of Koebner (KB) phenomenon due to masks were evaluated. Boyd and Nelder classification was adopted for Koebner phenomenon and Bizzozero's for KB intensity. A total of 229/873 (26.2%) dermatological consultations were KB triggered dermatoses and lesions were located in mask-covered ear area (76 [33.2%]), malar area (73 [31.8%]), perioral area (53 [23.1%]), and nose (27 [11.8%]). The first KB category grouped 142 patients (psoriasis, vitiligo, maskne, and mask rosacea), the second one 24 (warts, molluscum contagiosum, and impetigo), the third one 46 (atopic dermatitis), and the fourth one 17 (eczema). Among previously KB negative psoriatic patients that became KB positive, 9/13 (69.2%) had discontinued or modified the prescribed antipsoriatic treatment. Mask-related Koebner phenomenon is an important clinical sign to orient clinician's therapeutic protocols during COVID-19 pandemic, especially in patients with psoriasis.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".