Effect of flow rate on coarse particle mass (CPM) from a dry powder inhaler (DPI) and a metered dose inhaler (MDI) / spacer delivering salbutamol
Bibliographic record
Abstract
Objective: Spacers are regularly prescribed with MDI to help coordination and reduce oropharyngeal (OP) deposition caused by the aerosol velocity of the inhaler spray. DPIs require adequate inspiratory flow to aerosolize the powder and carry it past the OP. We undertook this study to determine if inspiratory effort could impart significant velocity to the powder particles that might lead to OP deposition. Methods: Easyhaler DPIs and Teva Salamol MDIs with AeroChamber2go* spacer were assessed by Next Generation Cascade Impactor at flow rates of 15 to 60 L/min. Salbutamol (model drug) was recovered quantitatively by HPLC. Results were expressed as mass >4.46µm (CPM), likely to deposit in the OP of a patient. Aerosol velocity measurements of DPI and MDI alone were made using high speed photography. Results: CPM (mean±sd) µg/actuation: erj;64/suppl_68/PA2114/TB1 T1 TB1 Flow rate (L/min) 15 30 60 DPICPM 65.9 +/- 3.1 55.1 +/- 5.6 51.8 +/- 6.1 MDI/SpacerCPM 7.0 +/- 0.4 5.7 +/- 0.5 1.7 +/- 0.2 Aerosol velocity measurements showed that the DPI plume was similar, if a little higher (6 vs 5 m/s) to the MDI without spacer. Conclusions: At all flow rates tested (sub optimal or optimal for powder dispersion) significant inertia was imparted to the DPI particles which would likely cause more than 50% of drug to impact in the OP. High speed photography comparisons at 10cm from the inhaler, representing the back of the throat, showed that DPI particle velocities were at least as high as MDI. This reinforces the recommendations to use a spacer with an MDI to reduce OP deposition (as shown in results) and minimize local and systemic side effects, an option not possible with the DPI.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".