Prioritising Patients and Planet: Advocating for Change in Respiratory Care
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,057 | 0,088 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,019 | 0,029 |
| Communication savante | 0,028 | 0,046 |
| Science ouverte | 0,006 | 0,037 |
| Intégrité de la recherche | 0,043 | 0,068 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,008 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».