Face Masks and Cough Etiquette Reduce the Cough Aerosol Concentration of <i>Pseudomonas aeruginosa</i> in People with Cystic Fibrosis
Bibliographic record
Abstract
RATIONALE: People with cystic fibrosis (CF) generate Pseudomonas aeruginosa in droplet nuclei during coughing. The use of surgical masks has been recommended in healthcare settings to minimize pathogen transmission between patients with CF. OBJECTIVES: To determine if face masks and cough etiquette reduce viable P. aeruginosa aerosolized during coughing. METHODS: Twenty-five adults with CF and chronic P. aeruginosa infection were recruited. Participants performed six talking and coughing maneuvers, with or without face masks (surgical and N95) and hand covering the mouth when coughing (cough etiquette) in an aerosol-sampling device. An Andersen Cascade Impactor was used to sample the aerosol at 2 meters from each participant. Quantitative sputum and aerosol bacterial cultures were performed, and participants rated the mask comfort levels during the cough maneuvers. MEASUREMENTS AND MAIN RESULTS: During uncovered coughing (reference maneuver), 19 of 25 (76%) participants produced aerosols containing P. aeruginosa, with a positive correlation found between sputum P. aeruginosa concentration (measured as cfu/ml) and aerosol P. aeruginosa colony-forming units. There was a reduction in aerosol P. aeruginosa load during coughing with a surgical mask, coughing with an N95 mask, and cough etiquette compared with uncovered coughing (P < 0.001). A similar reduction in total colony-forming units was observed for both masks during coughing; yet, participants rated the surgical masks as more comfortable (P = 0.013). Cough etiquette provided approximately half the reduction of viable aerosols of the mask interventions during voluntary coughing. Talking was a low viable aerosol-producing activity. CONCLUSIONS: Face masks reduce cough-generated P. aeruginosa aerosols, with the surgical mask providing enhanced comfort. Cough etiquette was less effective at reducing viable aerosols.
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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 | 0.000 | 0.002 |
| 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) | 0.003 | 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".