Effect of Facial Material Softness and Applied Force on Face Mask Dead Volume, Face Mask Seal, and Inhaled Corticosteroid Delivery Through an Idealized Infant Replica
Bibliographic record
Abstract
BACKGROUND: During the aerosol delivery device design and optimization process, in vitro lung dose (LD) measurements are often performed using soft face models, which may provide a more clinically relevant representation of face mask dead volume (MDV) and face mask seal (FMS) than hard face models. However, a comparison of MDV, FMS, and LD for hard and soft face models is lacking. METHODS: Metal, silicone, and polyurethane represented hard, soft, and very soft facial materials, respectively. MDV was measured using a water displacement technique. FMS was measured using a valved holding chamber (VHC) flow rate technique. The LD of beclomethasone dipropionate (BDP) delivered via a 100-μg Qvar® pressurized metered dose inhaler with AeroChamber Plus® Flow-Vu® VHC and Small Mask, defined as that which passes through the nasal airways of the idealized infant geometry, was measured using a bias tidal flow system with a filter. MDV, FMS, and LD were measured at 1.5 lb and 3.5 lb of applied force. A mathematical model was used to predict LD based on experimental measurements of MDV and FMS. RESULTS: Experimental BDP LD measurements for ABS, silicone, and polyurethane at 1.5 lb were 0.9 (0.6) μg, 2.4 (1.9) μg, and 19.3 (0.9) μg, respectively. At 3.5 lb, the respective LD was 10.0 (1.5) μg, 13.8 (1.4) μg, and 14.2 (0.9) μg. Parametric analysis with the mathematical model showed that differences in FMS between face models had a greater impact on LD than differences in MDV. CONCLUSIONS: The use of soft face models resulted in higher LD than hard face models, with a greater difference at 1.5 lb than at 3.5 lb. A lack of a FMS led to decreased dose consistency; therefore, a sealant should be used when measuring LD with a hard ABS or soft silicone face model at 1.5 lb of applied force or less.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".