Nonuniform Deposition of Pressurized Metered-Dose Aerosol in Spacer Devices
Bibliographic record
Abstract
BACKGROUND: Pressurized metered-dose inhalers (pMDIs) are commonly used to administer medication to patients suffering from chronic lower respiratory tract diseases such as asthma. Inhaling medication directly from a pMDI can prove difficult for some patients and, as a result, add-on devices (or spacers) have been designed to aid in the delivery of medication. Although spacers increase the percentage of medication that reaches the patient, medication will also nonsymmetrically deposit on the walls of the device and will be lost to the device itself. METHODS: The deposition of medication, salbutamol sulfate, within a large- and a small-volume spacer, has been studied through an experimental and numerical analysis. Experiments were conducted at inspiratory flow rates ranging from 30 to 60 L/min. The amount of deposition of the medication on the walls of the spacer was quantified through an application of spectrophotometry. Computational fluid dynamics was used to quantify the deposition numerically. Simulations were conducted by implementing mean flow and turbulent tracking of particles using unsteady Reynolds-averaged Navier-Stokes (URANS) equations with a shear stress transport turbulence model. Regions of deposition are of interest, as well as how the method of deposition varied for different inhalation flow rates. RESULTS AND CONCLUSIONS: spacers was found to be greater in the lower half as opposed to the upper half of the spacer due to a downward spray angle. With an increased flow rate, these spacers demonstrated a slight increase in medication delivered to the inline filter, which is analogous to that reaching the patient, and an increase in distal deposition. For the numerical analysis, the results indicated that inertial impaction is the most likely method of deposition for the Volumatic spacer, and turbulence is more likely to cause deposition in the OptiChamber spacer.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".