A re-usable, locally manufactured, half-face respirator provides better protection than fitted disposable N95 masks
Bibliographic record
Abstract
BACKGROUND The coronavirus disease 2019 pandemic has led to persistent shortages of respiratory protective equipment in many jurisdictions. Re-usable industrial respirators have been proposed and deployed as an alternative, but also face severe supply limitations. Numerous respirator designs have been proposed since the start of the current pandemic, but few have been systematically tested on healthcare workers (HCWs). OBJECTIVE In this paper, we describe a locally manufactured respirator named ‘Duo’ that includes separate, valved, inspiratory and expiratory pathways and accommodates up to two filters. Duo was compared with the disposable commercial N95 in a cohort of 41 HCWs using standardised quantitative fit-testing. DESIGN A prospective observational cross-sectional study. SETTING Conducted between May and June 2020 among HCWs at Toronto General Hospital. PARTICIPANTS Forty-one HCWs of both sexes working at Toronto General Hospital. MAIN OUTCOME MEASURES Quantitative fit-testing involving seven tests as prescribed by Canadian standard CSA Z94.4-18. We measured the median overall fit-factors; the individual manoeuvre fit-factors and pass rate. RESULTS The median [IQR] overall fit-factors were 2947 [2228 to 4405] and 77.2 [51.9 to 152.1] for the Duo and disposable N95 respirators respectively (P < 0.0001). The overall pass rate of quantitative fit-testing fitted disposable N95 was 58.5% (24/41), and 100% for the Duo. CONCLUSIONS A re-usable, locally manufactured, half-face respirator performs better than fitted disposable N95 masks as assessed by quantitative fit-testing. This can help address the global supply disruption for a better response to future pandemics. The device requires further modification and testing to optimise exhalation flow resistance, and full conformance with technical standards is required for regulatory approval.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".