Choice of Salbutamol Metered Dose Inhaler (MDI) type and use of a Spacer Impact Drug Delivery and Carbon Emissions
Bibliographic record
Abstract
Rationale: Asthma control and global warming are important issues that have significant impacts on human health and the environment. It is critical however to ensure that maintaining or improving the patient’s asthma control goes hand in hand with environmental actions. This lab study investigated how to optimize the modelled lung delivery while at the same time minimizing the carbon emissions from the MDI. Methods: Two different salbutamol MDIs (Ventolin, Teva-salbutamol) were investigated and tested alone and combined with an AeroChamber2go* Spacer, designed specifically for on the go use with reliever medications. Fine particle mass (FPM, < 4.7µm), the mass of drug in the size range potentially available for lung delivery was determined using a cascade impactor, performed with no delay following actuation, and HPLC assay. Drug delivery was equated to a potential relative carbon footprint based upon published claims [1]. Results: The FPM data and potential carbon emissions values are shown below. Conclusions: The use of this spacer with a lower carbon emitting salbutamol MDI has the potential to improve lung delivery and reduce carbon emissions. The selection of the Teva-salbutamol MDI delivered using the AeroChamber2go* Spacer could potentially reduce the number of actuations required for patient relief of symptoms, which could help contribute to an up to 4.5x reduction in the carbon emissions compared to using a Ventolin MDI product alone. [1] https://greeninhaler.org/
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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 | 0.001 |
| 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) | 0.003 | 0.001 |
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".