Prioritising Patients and Planet: Advocating for Change in Respiratory Care
Bibliographic record
Abstract
The global burden of respiratory diseases, particularly asthma and chronic obstructive pulmonary disease (COPD), continues unabated. Suboptimal management places a significant strain on both patients and urgent or emergency care services. With an ageing population in many countries, the demand for these services is set to increase further. At the same time, healthcare systems are striving to reduce their carbon footprint and achieve net zero emissions, as the healthcare sector is a significant contributor to carbon emissions worldwide. Although these two goals may appear contradictory, they need not be in conflict. This article reviews an industry-sponsored symposium held at the European Respiratory Society (ERS) Congress 2024 in Vienna, Austria, in September 2024. The session addressed the urgent need to change the delivery model for respiratory healthcare in response to the increasing prevalence of respiratory diseases and the challenges posed by climate change. Co-chair John Hurst, Professor of Respiratory Medicine at University College London (UCL), UK, underscored the importance of innovative solutions for managing respiratory diseases and highlighted the challenges faced by healthcare decision-makers. This was further elaborated on by Omar Usmani, Professor of Respiratory Medicine at Imperial College London, UK, who emphasised the importance of clinical choice. He stated that inhaled medicines, which form the cornerstone of treatment, should not be considered interchangeable. He also discussed ongoing efforts to maintain access to essential medicines by developing novel next-generation propellants (NGP) for pressurised metered-dose inhaler (pMDI) devices, which will reduce their carbon footprint to levels comparable with dry powder inhalers (DPI). Additionally, he described the European Chemicals Agency (ECHA) proposal to restrict a broad range of chemicals classed as per- and polyfluoroalkyl substances (PFAS). This precautionary measure would affect both current propellants in pMDIs and the transition to NGPs, with global implications for inhaled medicines. Erika Penz, Associate Professor of Respirology, Critical Care, and Sleep Medicine at the University of Saskatchewan, Canada, noted that suboptimal management of respiratory disease is associated with a disproportionately high burden on both patients and the environment. The forthcoming availability of pMDI medicines with NGPs alone will not resolve this larger issue. As every healthcare interaction carries a carbon footprint, which increases with the intensity of treatment, the implementation of guidelines into clinical practice would improve patient outcomes and reduce the demand on healthcare services and the associated carbon emissions. Co-chair Helen Reddel, Clinical Professor and Research Leader at the Woolcock Institute of Medical Research, Australia, concluded by re-emphasising the urgent need to implement guidelines immediately for the benefit of both patients and the environment.
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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 | 0.057 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.029 |
| Scholarly communication | 0.028 | 0.046 |
| Open science | 0.006 | 0.037 |
| Research integrity | 0.043 | 0.068 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".