Comparison of Three Valved Holding Chambers for the Delivery of Fluticasone Propionate–HFA to an Infant Face Model
Bibliographic record
Abstract
The purpose of this study was to compare three valved holding chambers (VHC) with facemasks attached. One VHC (AeroChamber Max[TM] with medium mask) was made with materials that dissipate surface electrostatic charge, and the others (OptiChamber Advantage and ProChamber[TM] with pediatric facemask) were made from non-conducting materials. The OptiChamber Advantage and ProChamber VHCs were each washed with an ionic detergent and drip dried before testing to minimize surface electrostatic charge. The AeroChamber Max VHCs were tested "out of the package" and also after wash, rinse, and drying. An infant face model incorporating an electrostatic filter in the oral cavity was connected to a breath simulator using a standard waveform for a small child. The fit of each VHC with facemask was demonstrated by agreement of inspiratory flow measurements between a pneumotachograph connected to the system with those set on the simulator. An HFA-fluticasone propionate metered dose inhaler (MDI; 125 microg/dose) was inserted into the VHC, two actuations were delivered, and the filters were subsequently assayed using high-pressure liquid chromatography (HPLC). Testing and sample assay order was randomized, and HPLC assays were undertaken blinded. Drug delivery efficiency expressed as a percentage of the total dose of fluticasone propionate (250 microg) for the AeroChamber Max VHC "out-of-the-package" was 22.0(0.7)% (mean [99% CI]) and 21.2(1.5)% when pre-washed/rinsed. Results for the pre-washed ProChamber and OptiChamber Advantage VHCs were 10.2(0.55)% and 8.8(1.9)%, respectively. The more efficient delivery of medication via VHCs made from electrostatic charge dissipative materials should be considered when choosing doses for small children.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".