Filtration performance, fit test and side effects of respiratory personal protective equipment following decontamination: Observations for user safety and comfort
Bibliographic record
Abstract
OBJECTIVE: While facing personal protective equipment (PPE) shortages during the COVID-19 pandemic, several institutions looked to PPE decontamination and reuse options. This study documents the effect of two hydrogen peroxide treatments on filtration efficiency and fit tests as well as the side effects for volunteers after the decontamination of N95 filtering facepiece respirators (FFRs). We also propose an efficient and large-scale treatment protocol that allows for the traceability of this protective equipment in hospitals during PPE shortages. METHODS: The effects of low-temperature hydrogen peroxide sterilization and hydrogen peroxide vapor (HPV) on two FFR models (filtration, decontamination level, residual emanation) were evaluated. Ten volunteers reported comfort issues and side effects after wearing 1h FFRs worn and decontaminated up to five times. RESULTS: The decontamination process does not negatively affect FFR efficiency, but repeated use and handling tend to lead to damage, limiting the number of times FFRs can be reused. Moreover, the recommended 24-h post-treatment aeration does not sufficiently eliminate residual hydrogen peroxide. Prolonged aeration time increased user comfort when using decontaminated FFRs. CONCLUSIONS: HPV and low-temperature hydrogen peroxide sterilization seem to be appropriate treatments for FFR decontamination when the PPE is reused by the same user. PPE decontamination and reuse methods should be carefully considered as they are critical for the comfort and safety of healthcare workers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".