Drug Delivery Performance and Fugitive Emission Comparison of Two Commercially Available Nebulizer Systems
Bibliographic record
Abstract
Background: Delivery of inhaled medications by nebulizer for the treatment of respiratory disease is widespread. Important factors to consider in a delivery system are amount and consistency of drug delivered to the lungs as well as the amount of drug/droplets that are emitted to the local environment (Fugitive Emissions). Methodology: Nebulizers (AEROECLIPSE* II Breath Actuated Nebulizer (BAN) and Aerogen* Ultra) were evaluated with 2.5mg/3.0mL fill of salbutamol and connected to a breathing simulator mimicking adult tidal volume (500-ml) with I/E ratios of 1:1, 1:2 and 1:3. Emitted aerosol was captured by filter at 1-minute intervals until sputtering to determine total mass (TMsal). The percentage of drug mass lost to the environment (ELsal) was determined by combining the TMsal recovered from the inhalation filters along with the residual mass recovered from the nebulizer and subtracting that from the initial 2.5mg salbutamol placed in the nebulizer. Salbutamol assay was undertaken by HPLC. Fine droplet mass (FDMSal µg) was determined by laser diffractometry as the product of TMsal and fine droplet fraction (%<4.7µm) Results: Average +/- SD FDMsal and ELsal at extended I/E ratios are reported in the table. Conclusions: Higher and more consistent delivery was achieved by BAN as well as lower fugitive emissions. Clinicians should be aware of the ability to get increased amounts of medication to the lungs while maintaining a safer work environment for staff with use of the BAN.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".