Evaluation of bioaerosol exposures during hospital bronchoscopy examinations
Bibliographic record
Abstract
During hospital bronchoscopy examinations, aerosols emitted from the patient's during coughing can be found suspended in the ambient air. The aerosols can contain pathogenic microorganisms. Depending on their size, these microorganisms can remain in the air for a long time. The objective of this study was to measure the sizes and concentrations of the biological and non-biological particles produced during bronchoscopy examinations, and to propose preventive or corrective measures. Two bronchoscopy rooms were studied. An aerodynamic particle sizer (UV-APS) was used to establish the concentrations of the particles present and their size distributions. This instrument determines the aerodynamic diameter of the aerosols and can distinguish fluorescent (bioaerosols) and non-fluorescent particles. Reference concentrations were measured before the start of the examinations (morning background concentrations). They were used as comparison levels for the concentrations measured during and at the end of the bronchoscopies. In parallel, computational fluid dynamics (CFD) made it possible to isolate and understand different factors that can affect the concentration levels in bronchoscopy rooms. The concentrations of the non-fluorescent and fluorescent particles (bioaerosols) were significantly higher (p ≤ 0.05) during the bronchoscopy examinations than the reference concentrations. For the investigated factors, the bioaerosol concentrations were significantly higher during bronchoscope insertion tasks. The time required at the end of the day for the bioaerosols to reach the morning reference concentrations was about fifteen minutes. The average particle sizes were 2.9 μm for the fluorescent particles (bioaerosols) and 0.9 μm for the non-fluorescent particles. Our models based on computational fluid dynamics (CFD) enabled us to observe the behaviour of aerosols for the different rooms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".