Respimat soft mist inhaler (SMI) in-vitro aerosol delivery with the ODAPT adapter and facemask
Bibliographic record
Abstract
INTRODUCTIONThe Respimat Soft Mist Inhaler (SMI) is a propellant-free inhaler that generates a fine aerosol mist suitable for inhalation. For patients requiring facemasks for medication delivery, the presence of the facemask influences the lung deposition. The purpose of this study was to assess, in vitro, the effect of the attachment of add-ons (ODAPT soft mist adapter with facemask) to the Respimat SMI on the medication delivery under different conditions and evaluate the efficacy of the ODAPT with facemask.METHODSThe Spiriva Respimat SMI was tested twice (with and without add-ons) at 28.3 L/min and 60 L/min in 40%–50% and >90% relative humidity environments, using an 8-stage Andersen cascade impactor, enclosed in a sealed temperature-and-humidity-controlled chamber. The particle deposition was assessed by UV-visible spectrophotometry.RESULTSIncreasing relative humidity shifts the particle size distribution toward larger particles due to the evaporation rate difference. At higher humidity levels, 18.7% and 20.3% of the medication delivered was lost in the add-ons at 28.3 L/min and 60 L/min, respectively. However, the fine particle fraction (FPF) was found to range between about 42% and 51% for 28.3 L/min and 41% and 50% for 60 L/min. No significant difference in FPF was found at different flow rates.CONCLUSIONMinimal impact therapeutic drug delivery was achieved when using the ODAPT adapter with facemask for the Spiriva Respimat SMI with a loss of medication deposition of 7.39% and 16.23% under normal and high relative humidity, respectively, at 28.3 L/min and 18.84% and 9.64% under normal and high relative humidity, respectively, at 60 L/min.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".