Are aerosols generated during lung function testing in patients and healthy volunteers? Results from the AERATOR study
Bibliographic record
Abstract
Pulmonary function tests are fundamental to the diagnosis and monitoring of respiratory diseases. There is uncertainty around whether potentially infectious aerosols are produced during testing and there are limited data on mitigation strategies to reduce risk to staff. Healthy volunteers and patients with lung disease underwent standardised spirometry, peak flow and FE NO assessments. Aerosol number concentration was sampled using an aerodynamic particle sizer and an optical particle sizer. Measured aerosol concentrations were compared with breathing, speaking and voluntary coughing. Mitigation strategies included a standard viral filter and a full-face mask normally used for exercise testing (to mitigate induced coughing). 147 measures were collected from 33 healthy volunteers and 10 patients with lung disease. The aerosol number concentration was highest in coughs (1.45–1.61 particles/cm 3 ), followed by unfiltered peak flow (0.37–0.76 particles/cm 3 ). Addition of a viral filter to peak flow reduced aerosol emission by a factor of 10 without affecting the results. On average, coughs produced 22 times more aerosols than standard spirometry (with filter) in patients and 56 times more aerosols in healthy volunteers. FE NO measurement produced negligible aerosols. Cardiopulmonary exercise test (CPET) masks reduced aerosol emission when breathing, speaking and coughing significantly. Lung function testing produces less aerosols than voluntary coughing. CPET masks may be used to reduce aerosol emission from induced coughing. Standard viral filters are sufficiently effective to allow guidelines to remove lung function testing from the list of aerosol-generating 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".