A reusable, locally manufactured, half- face respirator provides better protection than fitted disposable N95 masks: development and quantitative fit-testing comparison
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has led to persistent supply shortages of respiratory protective equipment in many jurisdictions. Reusable industrial respirators have been proposed and deployed as an alternative, but also face severe supply limitations. In addition, industrial respirators do not filter the user’s expired breath, a major limitation in health care settings where bidirectional protection is required. We present the development and quantitative testing of a reusable silicone respirator that can be locally manufactured using low-cost desktop infrastructure. Using standardized quantitative fit-testing (QNFT including resting and activity components according to CSA Z94.4-18) in a cohort of 41 healthcare workers (HCWs), we compared the performance of the mask to the individually-fitted disposable N95 masks that the HCWs had been assigned by our institution. Overall QNFT pass rates for disposable N95 respirators were 58.5% vs. 100% for the reusable mask. For a production run of 1000 masks, unit cost is approximately $25 CAD in materials and 35 minutes in labor per mask. The device requires further testing to assess flow resistance, carbon dioxide rebreathing, and full conformance with technical standards 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".