In vitro dose comparison of Respimat® Soft Inhaler™ with dry powder inhalers for COPD maintenance therapy
Bibliographic record
Abstract
Background: A head-to-head comparison of contemporary inhalers in real patients is difficult because of the variability inevitably introduced by the patients. Therefore in-vitro data on the dose passing the mouth-throat region was acquired using the realistic Alberta throat model. Three inhalers (Respimat®, Breezhaler®, and Genuair®) were compared. Providing flow profiles, the effect of the air flow rate can be well determined and its effect on the in vitro dose to the lung can be quantified. Methods: The flow resistances of all inhalers were determined. All three inhaler types were adapted to the inlet of the Alberta throat; inhaler outlets were centered with the mouth cavity and aligned with their axis parallel to the „tongue“. The devices were tested applying inhalation flow profiles idealized from measured profiles of COPD patients of different severities of disease inhaling through comparable flow resistances. Passing the air from the throat through a mixing inlet, the size classification was performed in a Next Generation Impactor at constant flow (100 L/min). Drug analysis was performed using HPLC. Results: The inhalers have different air flow resistances (Respimat: 0.04 Sqrt(mbar)*min/L, Breezhaler: 0.06 Sqrt(mbar)*min/L, Genuair: 0.1 Sqrt(mbar)*min/L). As the generation of aerosol is independent of air flow in the Respimat inhaler, only the simulation of very fast inhaling patients creates drug impact in the throat, while in most dry powder inhalers, a relatively high air flow is required for deagglomeration of the drug particles. This in turn entails higher deposition of all constituents of the powder formulations in the mouth-throat region. Conclusions: The resistance influences the patients who will probably inhale at different air flow using the different devices. The data suggest that the Respimat Soft Inhaler is an excellent inhaler which by its unique spray technology makes use of a large amount of the delivered drug.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".