Consistency of Medication Delivery with Breath Actuated Technology when Simulated with Differing Inhalation/Exhalation Ratios
Bibliographic record
Abstract
Rationale: Breath actuated technology (BA) allows for medication delivery to occur only on the inhalation phase of each breathing cycle, whereas breath enhanced technology (BE) delivers aerosol even during exhalation. We sought to compare the output of two high efficiency nebulizers at different inspiratory:expiratory (I:E) ratios simulating a patient breathing pattern with obstructive lung disease. Methods: Each nebulizer/compressor (AeroEclipse* XL BAN* (BA)/Ombra* Table Top, PARI LC Sprint (BE)/PARI BOY Classic) was filled with 2mL of 1mg/mL salbutamol sulphate and the mouthpiece connected to a breathing simulator (ASL 5000). Tidal volume was fixed at 500mL, but I:E ratio and rate/minute (BPM) were varied (Table). Aerosol was collected at minute intervals and drug recovered from the filter assayed by HPLC. That mass was multiplied by fine droplet fraction (%<4.7µm) measured via Spraytec laser diffractometer to calculate fine droplet mass (FDM). Results: Measures of FDM ((μg); mean ± SD) are summarized in the Table. FDM from the BA nebulizer was unaffected by changes in breathing pattern, whereas output from the BE nebulizer was lower and decreased with increasing I:E ratio Conclusions: More consistent dose delivery was achieved with the BA technology rather than BE. Delivering medication only when the patient inhales would result in more stable therapy if I:E ratio was to change with disease progression or if patient took breaks to talk during treatment.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".