Efficacy and Safety of Disinfectants for Decontamination of N95 and SN95 Filtering Facepiece Respirators: A Systematic Review
Bibliographic record
Abstract
Background: Decontaminating and re-using filtering facepiece respirators (FFR) for healthcare workers is a potential solution to address inadequate FFR supply during a global pandemic.Aim: The objective of this review was to synthesize existing data on the effectiveness and safety of using chemical disinfectants to decontaminate N95 FFR. Methods: We conducted a systematic review on disinfectants to decontaminate N95 FFRs 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, physical appearance, safety, or mask fit following decontamination with a disinfectant was included. Findings and Conclusions: A single cycle of vaporized hydrogen peroxide successfully removes infectious pathogens without affecting mask function or fit, and with little change in FFR physical appearance. Residual hydrogen peroxide levels following decontamination were below the safety limit. More than one decontamination cycle of vaporized hydrogen peroxide may be possible but further information is required on how multiple cycles would affect mask fit in a real world setting before the upper limit can be established. Although immersion in liquid hydrogen peroxide does not appear to adversely affect mask function, there is no available data on its ability to remove infectious pathogens from FFRs or its impact on mask fit. Sodium hypochlorite, ethanol, isopropyl alcohol and EtO are not recommended due to safety concerns or negative effects on mask function.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".