Facial dermatoses induced by face masks: A systematic review and <scp>meta‐analysis</scp> of observational studies
Bibliographic record
Abstract
The use of masks for infection control was common in the COVID-19 pandemic. As numerous cross-sectional studies have suggested a link between the use of such masks and various facial dermatoses, a systematic review and meta-analysis of published studies was conducted to evaluate this association, as well as potential risk factors for the development of such facial dermatoses. Observational studies were searched for in MEDLINE, EMBASE and the Cochrane Central Register. Thirty-seven observational studies with a total of 29 557 study participants were identified. This study was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 checklist and quality was assessed via the Newcastle-Ottawa Quality Assessment Scale., Overall prevalence of facial dermatoses was 55%. Individually, acne, facial dermatitis, itch and pressure injuries were consistently reported as facial dermatoses, with a pooled prevalence of 31%, 24%, 30% and 31%, respectively. Duration of mask-wear was the most significant risk factor for the development of facial dermatoses (95% CI: 1.31-1.54, p < 0.001). Overall, facial dermatoses associated with mask wear are common, and consist of distinct entities. They are related to duration of use. Appropriate and tailored treatment is important to improve the outcomes for these affected patients.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.022 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".