Medication delivery testing of valved holding chambers (VHCs) with facemask for infant use by means of a model infant face
Bibliographic record
Abstract
RATIONALE: This laboratory study used a model taken from a 7-month infant with soft tissue face modeling and an anatomically correct oro and nasopharynx to explore medication delivery differences achieved via three different VHCs with facemasks. METHODS: The following VHCs were evaluated (n=5/group): AeroChamber Plus* Flow-Vu* Anti-Static VHC with infant mask (AC-Plus FV); OptiChamber® Diamond® (OPT); InspiraChamber® with InspiraMask® (INS). The facemask of the VHC-on-test was applied to the face of the model with a clinically appropriate force of 0.9 kg, and the mask positioned to optimize seal to the face as much as possible. A breathing simulator (tidal volume=50 mL; 30 breaths/min; duty cycle=25%) was connected, via a filter, to the distal airway to capture aerosol, representing medication potentially available for lung delivery. Fluticasone propionate 50 μg/actuation pMDI was delivered to the VHC and 6 breaths were taken. Drug was recovered from the filter (EMcarina) and assayed using HPLC. RESULTS:EMcarinaand VHC retention (mean ± SD), normalized to a percentage of label claim dose/actuation, are summarized below. CONCLUSIONS:EMcarinawas highest for the AC-Plus VHC, which was significantly greater than for the INS VHC (t-test, p < 0.0001). The results correlated well with previously reported data that indicated differences in face to facemask leakage for the same VHCs / Facemasks, and therefore highlights the importance of a good facemask to face seal in order to be able to effectively deliver drug to the infant9s lungs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".