Maximizing the benefit from the Inhaler In Order to Minimize Carbon Footprint
Bibliographic record
Abstract
Objective: To compare the modelled lung delivery of rescue medication via different valved spacers with the goal of providing optimum patient care and minimizing potential carbon footprint. Methods: 3 different spacers types were evaluated by breathing simulator (tidal volume=155-mL, I:E ratio=1:2, rate=25 cycles/min). The facemask of each spacer (n=3) was attached to an anatomical model and the airway coupled to a breathing simulator via a filter to capture drug particles that penetrated as far as the carina. 5-actuations of salbutamol (Ventolin Evohaler) were delivered at 30-s intervals and recovered from specific locations in the aerosol pathway by HPLC. Comparisons were then made on drug delivery data looking at potential dose to the lungs for each pMDI/spacer. This potential delivery was then equated to a potential relative carbon footprint based upon published claims [1] that Ventolin has a carbon footprint of 28 kg CO2 per inhaler. Results: The mass (µg) of salbutamol delivered to modelled carina are reported in the table. Conclusion: Depending on the pMDI/spacer system chosen the delivery of medication can vary significantly and as a result will have implications on the potential carbon footprint. In this case, the use of the AeroChamber Plus* Flow-Vu* spacer could potentially reduce the carbon footprint by three fold compared to the alternative spacers. By maximizing the amount of each puff reaching the lungs the patient is likely to be able to get relief sooner and reduce the amount of puffs needed. [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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".