P267 Assessment of the impact of nebuliser type on fugitive emissions in a treatment room when delivering inhaled medications
Bibliographic record
Abstract
Rationale Nebulisers are widely used to deliver inhaled medications. Fugitive emissions are aerosols created by the nebuliser that are not inhaled by the patient. Fugitive emissions along with nebuliser exudate may potentially contain viral pathogens which can pose a risk to others in close proximity. A laboratory investigation was performed to assess the potential for exposure to fugitive emissions within a treatment room. Methods Fugitive emissions produced by 3 different nebuliser types (AeroEclipse* II BAN* Nebuliser -breath-actuated, NebuTech† HDN† breath-enhanced, and Aerogen† Ultra-vibrating mesh) were assessed using salbutamol as a tracer and simulating an adult receiving a nebuliser treatment. Aerosol filters attached to breathing simulators were positioned 0.8 and 2 meters away from the nebuliser to represent caregivers within the room. Collection areas were also established within the test room to measure the amount of fugitive emissions/exudate that may have reached the caregiver but were not inhaled. The mass of salbutamol recovered from filters and collection surfaces was analysed by HPLC. Results Total recovered emissions/exudate varied depending on the type of nebuliser used. The BAN* Nebuliser produced the lowest amount of fugitive emissions. The vibrating mesh nebuliser produced a significantly higher recovered amount directly below the nebuliser compared to the other two devices. The distance between the patient and the healthcare worker was not determined to be a significant factor in the amount of exposure to fugitive emissions. Conclusions Healthcare workers should be aware of the potential risks associated with fugitive emissions from nebulisers and ways to minimise exposure. The choice of nebuliser can significantly affect the amount of fugitive emissions and exudate produced in a treatment environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".