The carbon footprint of the vascular surgery operating room
Notice bibliographique
Résumé
Objective: Climate change is the single greatest threat to global human health, contributing to changes in disease patterns, water and food insecurity, vulnerability in shelter and human settlements, climatic volatility, and population growth and migration.Health care generates between 8% and 10% of all greenhouse gas emissions.Surgical care accounts for a significant proportion owing to equipment and drug use, sterility requirements, and life support systems.There are currently minimal data describing this carbon consumption.The purpose of this study was to determine the carbon use of common vascular interventions and to identify variables associated with increased carbon use. Methods:We conducted an observational study of all elective and urgent vascular surgery procedures performed at our institution over a 2-month period in 2023.All waste generated was weighed and cataloged per hospital waste practices.Additional characteristics of the procedures were also collected.Carbon use was determined by applying DEFRA greenhouse gas life-cycle conversion factors to the mean waste produced.These factors take into account greenhouse gas emissions generated in the initial production and eventual disposal.Variables associated with increased carbon production were evaluated by linear regression models with log-transformed outcomes.Results: Fifty-nine procedures were included.Complex endovascular aortic procedures were the most carbon intensive (69.35 kg CO 2 ), equivalent to driving a medium-sized vehicle 369 km.Dialysis access procedures (11.5 kg CO 2 ) and minor amputations (10.6 kg CO 2 ) were the least carbon intensive.Open surgical bypasses (28.4 kg CO 2 ) and cerebrovascular procedures (20.3 kg CO 2 ) produced a moderate amount of carbon.Endovascular interventions were 50% more carbon intensive than open interventions (95% confidence interval, 28%-77%; P < .01).Aortic interventions were 63% more carbon intensive than nonaortic interventions (95% confidence interval, 28%-109%; P < .01).In both models, there was nearly a one-half percent increase in carbon generated for each minute of additional operating time (P < .01).Blood loss was not consistently associated with carbon use.Conclusions: Climate change is a major threat to human health.The environmental impact of day-to-day surgery is rarely considered but clearly significant.Larger procedures are associated with increased carbon use, particularly if they are endovascular or aortic.The duration of the case is also associated with increased use.Further work should be done to identify additional variables and opportunities for carbon reduction.(
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».