A systematic review of passing fit testing of the masks and respirators used during the COVID-19 pandemic: Part 1-quantitative fit test procedures
Bibliographic record
Abstract
BACKGROUND: During respiratory infection pandemics, masks and respirators are highly sought after, especially for frontline healthcare workers and patients carrying respiratory viruses. The objective of this study was to systematically review fit test pass rates and identify factors influencing the fitting characteristics. METHODS: Potentially relevant studies were identified using PubMed, Scopus, Web of Science, and Science Direct during the COVID-19 pandemic from February 5, 2020, to March 21, 2023. The search strategy using the following keywords was conducted: Quantitative Fit Test, Condensation Nuclei Counter, Controlled Negative Pressure, PortaCount, Sibata, Accufit, Fit, Seal, Mask, Respirator, Respiratory Protective Device, Respiratory Protective Equipment, Protective Device, Personal Protective Equipment, COVID-19, Coronavirus, and SARS-CoV-2. The quality of the included studies was also assessed using the Newcastle-Ottawa scale. RESULTS: A total of 137 articles met the eligibility criteria. Fifty articles had a quality score of less than 7 (good quality). A total of 21 studies had a fit test pass rate of less than 50%. 26 studies on disposable respirators and 11 studies on reusable respirators had an FF of less than 50 and less than 200, respectively. The most influential factors include respirator brand/model, style, gender, ethnicity, facial dimensions, facial hair, age, reuse, extensive movement, seal check, comfort and usability assessment, and training. CONCLUSION: 37.36% of the disposable respirator studies and 43% of the reusable respirator studies did not report fit test results. 67.86% of the disposable respirator studies had a fit test pass rate greater than 50%, and 35.84% of these studies had an FF greater than 100. Also, 85.71% of the reusable respirator studies had a fit test pass rate greater than 50%, and 52.77% of these studies had an FF greater than 1000. Overall, the fit test pass rate was relatively acceptable. Newly developed or modified respirators must undergo reliable testing to ensure the protection of HCWs. Subject and respirator characteristics should be considered when implementing fit testing protocols. An optimal fit test panel should be developed prior to respirator design, certification, procurement decisions, and selection procedures.
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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 | 0.016 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".