Interchanging Reusable and Disposable Nebulizers Used with Home-Based Compressors May Result in Inconsistent Dosing: A Laboratory Investigation with Device Combinations Supplied to the US Healthcare Environment
Bibliographic record
Abstract
Reusable nebulizer–compressor combinations deliver inhaled medications for patients with chronic lung diseases. On hospital discharge, the patient may take home the disposable nebulizer that was packaged and combine it with their home compressor. Though this practice may reduce waste, it can increase variability in medication delivery. Our study compared several reusable and disposable nebulizers packaged with compressor kits used in the US. We included a common disposable hospital nebulizer that may not be supplied with popular home kits but may be brought home after a hospitalization or emergency department visit. We focused on fine droplet mass < 4.7 μm aerodynamic diameter (FDM <4.7 μm ), associated with medication delivery to the airways of the lungs. We evaluated the following nebulizer–compressor combinations ( n = 5 replicates): OMBRA® Table Top Compressor with MC 300® reusable and Airlife™ MistyMax™ 10® disposable nebulizer, Sami-the-Seal® compressor with SideStream® reusable and disposable nebulizers and Airlife™ MistyMax 10™ disposable nebulizer, VIOS® compressor with LC Sprint® reusable, and VixOne® and Airlife™ MistyMax™ disposable nebulizers, Innospire® Elegance® compressor with SideStream® reusable and disposable nebulizers and Airlife™ MistyMax 10™ disposable nebulizer, Willis-the-Whale® compressor with SideStream® reusable and disposable nebulizers and Airlife™ MistyMax 10™ disposable nebulizer, Pari PRONEB® Max compressor with LC Sprint® reusable and Airlife™ MistyMax 10™ disposable nebulizer. We placed a 3-ml albuterol solution (0.833 mg/ml) in each nebulizer. A bacterial/viral filter was attached to the nebulizer mouthpiece to capture emitted medication, with the filter exit coupled to a simulator of a tidal breathing adult (rate = 10 cycles/min; V t = 600 ml; I/E ratio = 1:2). The filter was replaced at 1-min intervals until onset of sputter. Droplet size distributions ( n = 5 replicates/system) were determined in parallel by laser diffractometry. Cumulative FDM <4.7 μm varied from 381 ± 33 μg for the best performing combination (Proneb/LC-Sprint) to 150 ± 21 μg for the system with the lowest output (VIOS®/MistyMax 10™). Substituting one nebulizer for another can result in large differences in medication delivery to the lungs.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".