<i>In Vivo–In Vitro</i> Comparison of Deposition in Three Mouth–Throat Models with Qvar <sup>®</sup> and Turbuhaler <sup>®</sup> Inhalers
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it