Use of Extrathoracic Deposition Models for Patient-Specific Dose Estimation during Inhaler Design
Bibliographic record
Abstract
The lung dose of inhaled pharmaceutical aerosol that an individual will receive from an inhaler can now be more accurately estimated in light of recent extrathoracic deposition modeling that has correlated characteristic airway dimensions with deposition. This paper first summarizes the current state of extrathoracic deposition models, including recent developments that have quantified the effects of aerosol electrostatics and inhaler mouthpiece diameter on deposition. A generalized equation for predicting extrathoracic deposition in different subjects is then developed and average characteristic airway dimensions representative of different age groups are indicated. A methodology is then presented to predict the lung dose per unit body surface area individuals will receive from an inhaler. A sample calculation shows that a typical 10-year-old child subject would receive a lower lung dose per unit body surface area than an adult subject inhaling through the same inhaler at the same 90 L min-1 flow rate, due to greater extrathoracic deposition in the child. In order to provide an equivalent lung dose per unit body surface area to the child as to the adult, an inhaler particle size adjustment is specified. Finally, the use of idealized geometries for developing inhaler-specific empirical correlations and improving upon inhaler design is outlined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".