Aerosol generation during pulmonary function testing: Monitoring during different testing modalities
Bibliographic record
Abstract
Rationale Severe acute respiratory syndrome caused by coronavirus 2 (SARS-CoV-2) was declared a pandemic on March 11, 2020. Countries entered lockdown, restricting medical activities to essential services. Pulmonary function tests (PFT) are crucial for management of lung diseases. With limited data regarding aerosol generation and the risk of disease transmission during PFTs, many laboratories closed. Our objective is to quantify aerosol generation during different PFT modalities.Methods We measured aerosol particles in the 0.3-10.0 µm range with an Optical Particle Sizer (Model 3330; TSI Incorporated) and collected bioaerosols to detect respiratory pathogens during clinically indicated PFTs at a hospital-based laboratory during 2 time points in 2020.Results We monitored 81 and 41 individual multi-modality PFT sessions in June/July and December, respectively. Slow vital capacity, forced vital capacity and diffusion capacity generated higher aerosol counts compared to pre- and post-test room levels although all modalities were lower than during talking or coughing. The aerosol sizes generated were primarily 2.5-10 µm. Oscillometry generated higher overall concentrations than room sampling, also primarily in the 2.5-10 µm aerosols. The bioaerosol filters revealed no respiratory viruses or bacteria.Conclusions While PFT can generate aerosols, it is less than normal speech with the exception of PFT-induced coughing. Our findings suggest the risk of SARS-CoV-2 transmission is not increased and support the re-opening of PFT laboratories that adhere to universal masking, use of personal protective equipment and stringent infection control protocols. We strongly endorse adherence to public health guidelines in the operation of PFT laboratories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".