Effect of Flow Rate on Emitted Fine Particle Mass (FPM) from a Dry Powder Inhaler (DPI) and a Metered Dose Inhaler (MDI) with Spacer Delivering Salbutamol.
Bibliographic record
Abstract
Objective: DPIs are widely prescribed for the treatment of asthma and COPD because they are perceived to be portable and easy to use. Recently they have been seen as avoiding greenhouse gas emissions from propellants used in pMDIs. However, DPIs rely on patient inhalation technique to aerosolize the powder and transport it past the upper airway. To assess how particle size and dose might be affected by patients that do not have the capability to generate sufficient inspiratory flow rates our study evaluated a DPI and a pMDI plus a spacer (designed for on-the-go use) at a range of flow rates. Methods: Easyhaler DPIs and Teva Salamol pMDIs with AeroChamber2go* spacer were assessed by Next Generation Cascade Impactor at flow rates of 15 to 60 L/min. Salbutamol was recovered quantitatively by HPLC. Results were expressed as mass <4.46µm FPM as potentially available to the lungs of the patient. Results: Fine Particle Mass (mean±sd) micrograms (µg/actuation): erj;64/suppl_68/PA2110/TB1 T1 TB1 Flow Rate 15L/Min 30L/Min 60L/Min DPIFPM 1.7±0.1 13.1±1.3 30.0±3.5 pMDI/SpacerFPM 58.7±3.7 59.2±4.9 66.0±6.3 Conclusions: These data suggest a loss of performance in delivering respirable particles at both lower flowrates from the DPI, whereas little to no change occurred in FPM with the pMDI+spacer and it was approximately twice as efficient. Many patients may have the physiological capability to generate sufficient inspiratory flow rates close to 60 L/min. However, those at the extreme age range of potential users and those of all ages with severe obstructive disease or who lack the cognitive ability may fail to achieve sufficient delivery from 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 | 0.002 | 0.003 |
| 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) | 0.005 | 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".