Decontaminating N95 masks with Ultraviolet Germicidal Irradiation (UVGI) does not impair mask efficacy and safety: A Systematic Review
Bibliographic record
Abstract
Background: Inadequate supply of filtering facepiece respirators (FFR) for healthcare workers during a global pandemic such as the novel coronavirus outbreak (SARS-CoV-2) is a serious public health issue. Aim: The objective of this review was to synthesize existing data on the effectiveness of ultraviolet germicidal irradiation (UVGI) on N95 FFR decontamination. Methods: We conducted a systematic review on UVGI in N95 FFRs by using Embase, Medline, Global Health, Google Scholar, WHO feed, and MedRxiv. Two reviewers independently determined study eligibility and extracted and verified predefined data fields. Original research reporting on N95 FFR function, decontamination, or mask fit following UVGI were included. Findings and Conclusions: Twelve studies were identified, comprising of 53 different UVGI intervention arms and 43 N95 FFR models. In all cases, FFRs maintained National Institute for Occupational Safety and Health (NIOSH) certification standards following UVGI. Aerosol penetration averaged 1.19% (0.70-2.48%) and 1.14% (0.57-2.63%) for control and UVGI arms respectively. Airflow resistance for the control arms averaged 9.79 mm H2O (7.97-11.70 mm H2O) vs 9.85 mm H2O (8.33-11.44 mm H2O) for UVGI treatment arms. All UVGI protocols employing a cumulative dose >20,000 J/m2 resulted in a 2 log reduction in viral load. A >3 log reduction was observed in 7 UVIG arms using a dose >40,000 J/m2. Impact of UVIG on fit was evaluated in two studies (16,200; 32,400 J/m2) and did not find evidence of compromise. Altogether, our findings suggest that further work in this area should use a cumulative UV-C dose of 40,000 J/m2 or greater, and confirm appropriate mask fit following decontamination. (Final Version of this manuscript published in the Journal of Hospital Infection: O'Hearn et al, Journal of Hospital Infection 106 (2020) 163-175, https://doi.org/10.1016/j.jhin.2020.07.014)
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".