Comparison of Deposition in the USP and Physical Mouth–Throat Models with Solid and Liquid Particles
Bibliographic record
Abstract
BACKGROUND: Experimental deposition was studied using three different mouth-throat models: (1) the standard United States Pharmacopeia induction port (IP), (2) the idealized human mouth and throat replica developed by the University of Alberta (UofA replica), and (3) the conductive rubber mouth-throat cast from a human subject developed by Lovelace Respiratory Research Institute (LRRI cast). METHODS: Both solid and liquid monodispersed fluorescent particles in the size range of 2-30 μm in diameter were delivered into the devices at flow rates of 15, 30, and 60 L min(-1). For solid particles, the study was conducted with and without grease coating inside the devices to investigate the effects of particle bounce. CONCLUSIONS: Large amounts of rebounded particles were found for the IP and UofA replica without the coating treatment, while particle bounce was only observed at the large particle size for the LRRI cast. The UofA replica and LRRI cast agreed well for solid particles with coating treatments and liquid particles. The deposition results from this study were also compared to data of in vivo deposition studies from the literature. The deposition efficiencies in the UofA replica and LRRI cast were within the range of in vivo data, which showed a large scatter.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".