Metered Dose Inhaler (MDI) with Valved Holding Chamber (VHC) vs Dry Powder Inhalers (DPIs): Using Functional Respiratory Imaging (FRI) to Assess Modelled Lung Deposition in an Asthmatic Patient
Bibliographic record
Abstract
RATIONALE: Both MDIs and DPIs can be used to deliver drugs to manage Asthma. VHCs can be used to help patients with inhalation coordination of their MDIs. Inspiratory flow rate is known to influence drug delivery. This FRI based study assessed the modelled airway drug delivery from an MDI/VHC system and two DPI systems at optimal and sub optimal flow rates. METHODS: Three dimensional geometries of airways and lobes were extracted from a CT scan of a 21 year old male Asthma (moderate) patient. Drug delivery and airway deposition of MDI/VHC delivered albuterol (Ventolin HFA) was modelled using FRI with measured particle and plume characteristics via an AeroChamber Plus Flow-Vu VHC. Symbicort (budesonide/formoterol) Turbohaler and Seretide (fluticasone/salmeterol) Diskus DPIs were similarly modelled. Inhalation flow rates of 30 L/min (optimum for MDI/VHC, sub optimal for DPIs) and 60 L/min (optimum for DPIs, sub optimal for MDI/VHC) were assessed. RESULTS: The modelled lung deposition results are shown in the chart, expressed as a percentage of label dose, using both optimal and sub-optimal inhalation flow rates. CONCLUSIONS: The FRI deposition profiles highlight that the MDI/AeroChamber Plus Flow-Vu VHC system delivered an appreciably greater percentage of drug to the lung region than either of the two DPIs. The influence of inhalation flow profile was less with the MDI/VHC system and differed between the two DPIs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".