<i>In Vivo–In Vitro</i> Comparison of Deposition in Three Mouth–Throat Models with Qvar <sup>®</sup> and Turbuhaler <sup>®</sup> Inhalers
Bibliographic record
Abstract
In vitro polydisperse aerosol deposition in three mouth-throat models, namely, the USP (United States Pharmacopeia) mouth-throat (induction port), idealized mouth-throat, and highly idealized mouth-throat, was investigated experimentally. Aerosol particles emitted from two commercial inhalers, Qvar (pMDI) and Turbuhaler (DPI), were used. The in vitro deposition results in these three mouth-throat models were compared with in vivo data available from the literature. For the DPI, mouth-throat deposition was 57.3 +/- 4.5% for the USP mouth-throat, 67.8 +/- 2.2% for the idealized mouth-throat, and 69.3 +/- 1.1% for the highly idealized mouth-throat, which are all relatively close to the in vivo value of 65.8 +/- 10.1%. In contrast, for the pMDI, aerosol deposition in the idealized mouth-throat (25.8 +/- 4.2%) and the highly idealized mouth-throat (24.9 +/- 2.8%) agrees with the in vivo data (29.0 +/- 18.0%) reported in the literature better than that for the USP mouth-throat (12.2 +/- 2.7%). In both cases, the USP mouth-throat gives the lowest deposition among the three mouth-throat models studied. In summary, both the idealized mouth-throat and highly idealized mouth-throat improve the accuracy of predicted mean in vivo deposition in the mouth-throat region. This result hints at the potential applicability of either the idealized mouth-throat or highly idealized mouth-throat as a future USP mouth-throat standard to provide mean value prediction of in vivo mouth-throat deposition.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".