Ecological footprint of salbutamol administration by metered-dose inhaler versus nebulisation in acute asthma: a life-cycle assessment
Bibliographic record
Abstract
Objective The scientific evidence indicates little or no difference in the effectiveness or cost of using of metered-dose inhalers (MDIs) versus nebulisation to treat acute asthma in the emergency department (ED). However, the use of MDIs raises questions of environmental impact. Our objective was to compare the ecological footprint of salbutamol administered by MDI versus nebulisation. Design Life cycle assessment in which we inventoried and quantified the resources extracted and pollutants emitted by each therapeutic option, from the manufacturing of medication and equipment to their disposal by incineration. Setting EDs of the CHU de Québec-Université Laval (Canada). Participants Not applicable. Main outcome measures Each item of life cycle inventory data was translated into CO 2 -equivalent emissions (CO 2 eq) using the IPCC2021/GWP100 method. Results were estimated for the administration of one and three treatments of 800 µg of salbutamol by MDI and 5 mg by nebulisation (standard doses for adults and children ≥ 24 kg). Results One and three ED-administered treatments with salbutamol emit respectively 1.9 and 4.0 kg of CO 2 eq via MDI versus 0.9 and 1.0 kg via nebulisation, which corresponds to 5.5 and 11.6 km and to 2.7 and 2.8 km travelled in a subcompact car. Each series of eight inhalations from an MDI releases 1.1 kg of CO 2 eq due to emission of the hydrofluoroalkane propellant. Conclusions Considering the absence or minimal difference in clinical effectiveness, this study suggests that nebulisation may be a more eco-efficient administration route than MDIs in the emergency treatment of asthma. Trail registration: N/A
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".