Efficacy and Safety of Disinfectants for Decontamination of N95 and SN95 Filtering Facepiece Respirators: Protocol for a Systematic Review
Bibliographic record
Abstract
During the COVID-19 pandemic, a shortage of personal protective equipment (PPE), namely surgical masks, N95 masks, and gowns, has been experienced by some hospitals and could be expected in others due to a rapidly increased need. One method of addressing the shortage is to decontaminate and re-use PPE. The Centres for Disease Control (CDC) specifically recommends N95 filtering facepiece respirators (FFRs) for healthcare workers who are interacting with patients with COVID-19. There are anecdotal reports and published literature evaluating the potential of using disinfectants, such as hydrogen peroxide and bleach to decontaminate FFRs, with mixed reports of impact on structural integrity. To date this literature has not been comprehensively synthesized and the purpose of this review is to systematically review the existing literature on the use of disinfectants for the decontamination of facemask PPE.This information will be used to contribute to FFR decontamination protocols at the Children’s Hospital of Eastern Ontario and shared with other hospitals in Ontario, Canada, and internationally.
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 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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.017 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".