Decontamination Interventions for the Reuse of Surgical Mask Personal Protective Equipment: A Systematic Review
Bibliographic record
Abstract
Background: The high demand for personal protective equipment (PPE) during the novel coronavirus outbreak has created global shortages and prompted the need to develop strategies to conserve supply. Surgical mask PPE have a broad application of use in a pandemic setting, but little is known regarding decontamination interventions to allow for their reuse. Objective: Identify and synthesize data from original published studies evaluating interventions to decontaminate surgical masks for the purpose of reuse. Methods: We searched MEDLINE, Embase, CENTRAL, Global Health, the WHO COVID-19 database, Google Scholar, DisasterLit, preprint servers, and prominent journals from inception to April 8, 2020 for prospective original research on decontamination interventions for surgical mask PPE. Citation screening was conducted independently in duplicate. Study characteristics, interventions, and outcomes were extracted from included studies by two independent reviewers. Outcomes of interest included impact of decontamination interventions on surgical mask performance and germicidal effects. Results: Seven studies met eligibility criteria: one evaluated the effects of heat and chemical decontamination interventions applied after mask use on mask performance, and six evaluated interventions applied prior to mask use to enhance antimicrobial properties and/or mask performance. Mask performance and germicidal effects were both evaluated in heterogenous test conditions across a variety of mask samples (whole masks and pieces or individual mask layers). Safety outcomes were infrequently evaluated. Mask performance was best preserved with dry heat decontamination. Germicidal effects were best in salt-, N-halamine- and nanoparticle-coated masks. Conclusion: There is limited evidence on the safety or efficacy of surgical mask decontamination. Given the heterogenous methods used in the studies to date, we are unable to draw conclusions on the most appropriate, safest intervention(s) for decontaminating surgical masks for the purpose of reuse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".