Combining inhalation by a breath-actuated nebulizer (BAN) with exhalation with oscillating positive expiratory pressure device (OPEP) offers potential for simultaneous therapy
Bibliographic record
Abstract
RATIONALE: Secretion mobilization by OPEP is often given separately to inhaled medication. Combining a nebulizer (AeroEclipse®-II, Trudell Medical International (TMI), London Canada) with OPEP (Aerobika*, TMI), both therapies can be delivered simultaneously. We investigated to see if the stand-alone BAN output is affected by use with the Aerobika* device, or by substituting another OPEP product (acapella®, Smiths Medical, UK) METHODS: A Next Generation Impactor operated at 15 L/min was used to make droplet size measurements. The BAN (3 x 3 replicates/device) was operated by compressed air at 50 psig and filled with 2-mL budesonide suspension (0.25 mg/ml, Nebuamp®, AstraZeneca), and connected directly to the Ph.Eur. induction port. The measurements were repeated (a) with the Aerobika® OPEP device inserted between the BAN and induction port, and (b) substituting the acapella® OPEP. The BAN was run to sputter, and the therapeutically beneficial fine particle mass < 5.4 µm diameter (FMbud) determined. RESULTS: FMbud (mean ± SD) via the BAN alone, with the BAN-Aerobika®, and the BAN- acapella® OPEP devices were 278±8, 250±21 and 56±9 µg respectively. The BAN-Aerobika® combination marginally reduced delivery (paired t-test, p = 0.002), whereas the BAN-acapella® configuration resulted in substantial losses (p < 0.001). CONCLUSIONS: The AeroEclipse®-II BAN-Aerobika* combination offers combined aerosol/OPEP therapy with minimal medication loss. Substitution with the acapella® OPEP to deliver aerosolized medication results in substantial reduction in BAN-output that may have adverse clinical implications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".