Clinically Appropriate Testing of Valved Holding Chambers with Facemask - Impact of Substitution
Bibliographic record
Abstract
Rationale: To effectively evaluate VHCs that incorporate a facemask the most appropriate method is to use a face model that includes soft tissue simulation and an anatomically realistic oro-naso-pharynx. We report a study in which several VHCs (n=3/group) intended for use with children were evaluated using the ADAM III anatomical model of a 4 year old child. Methods: Each VHC was evaluated by breathing simulator mimicking a short coordination delay of 2s before starting to inhale, followed by tidal breathing (tidal volume=155-mL, I:E ratio=1:2, rate=25 cycles/min). The facemask of the VHC was attached to the anatomical model and the airway coupled to the breathing simulator via a filter to capture drug particles that penetrated as far as the carina. 5-actuations of fluticasone propionate (FP, Flovent 50) were delivered at 30-s intervals and recovered from specific locations in the aerosol pathway by HPLC. Results: The distribution of FP for each VHC is summarized in the table. Conclusions: Significantly more FP was delivered to filter/carina with the AeroChamber Plus® VHC (un-paired t-test, p < 0.001). Many factors could have accounted for this difference, such as chamber shape, material and mask seal. It is important that clinicians and pharmacists are aware that large differences in delivery efficiency may exist and therefore substitution should be avoided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".