In Vitro Estimation of Tracheobronchial and Alveolar Doses Using Filters
Bibliographic record
Abstract
To date, in vitro estimation of doses delivered by an inhaler to the different major regions of the lung has required combining particle size measurements of the inhaled aerosol with in silico deposition models. Such a two step process is labor and time intensive. Here, we describe instead the development of an apparatus that allows direct estimation of regional lung deposition by measurement of doses collected on purpose-built metal grid filters that mimic tracheobronchial deposition efficiency. Placing these filters downstream of the Alberta Idealized Throat and upstream of a final filter allows collection of doses depositing in the extrathoracic, tracheobronchial and alveolar regions. Artificial electrostatic deposition on the metal tracheobronchial filters is prevented by a custom inline electrostatic neutralizer. We use the resulting apparatus to estimate regional deposition with a variety of dry powder inhalers during realistic, time-varying inhalation maneuvers and three pMDIs with a constant flow rate of 30 l/min. These results are compared to those obtained with the traditional two step approach that combines cascade impaction with a regional deposition model. Good agreement is found between the two approaches, indicating that the present direct method may be an efficient, time-saving alternative method for in vitro estimation of regional lung doses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".