S64 Impact of choice of salbutamol pMDI and use of spacer on drug delivery and emissions – best for patient and environment
Bibliographic record
Abstract
Introduction and Objectives MDIs are an important device option for many respiratory patients. The addition of a spacer can improve lung deposition, overcome coordination issues and reduce side effects caused by oropharyngeal deposition. As current MDIs contain hydrofluorocarbon propellants, it would be beneficial to find ways to reduce carbon emissions without compromising patient safety. This lab study investigated a way to optimize the modelled lung dose per actuation while at the same time minimizing the carbon emissions from the MDI. Methods Two different salbutamol 100 mcg MDIs were investigated, Ventolin (GSK) and Salamol (Teva), both available in the UK market. Each was tested alone and combined with an AeroChamber Plus* Flow-Vu* Spacer (TMI). Fine particle mass (< 4.7 microns), therefore the mass of drug in the size range potentially available for lung delivery, was determined using an abbreviated cascade impactor, performed with no delay following actuation, and HPLC assay. Carbon emissions per actuation were also determined. Results The carbon emissions per actuation were available for Ventolin and Salamol from a reference source. The results are reported in the table below. The fine particle mass data of the different configurations are also shown in the table. A key point to note is the increase of delivery from 32.5 mcg/actuation for Ventolin alone to 54.4 mcg/actuation for Salamol delivered with the spacer. Conclusions The use of the spacer with a lower carbon emitting salbutamol MDI has the potential to improve lung delivery and reduce carbon emissions. In combination, the selection of the Salamol MDI delivered using the AeroChamber Plus* Flow-Vu* Spacer could potentially reduce the number of actuations required for patient relief of symptoms, which could help contribute to an up to 4 times reduction in the carbon emissions compared to using a Ventolin MDI product alone. Please refer to page A210 for declarations of interest related to this abstract.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.039 | 0.004 |
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".