The science of climate change and the effect of anaesthetic gas emissions: a reply
Bibliographic record
Abstract
Physicians are not climate scientists and are not trained in the intricacies of how greenhouse gases affect climate change. Hence it is refreshing to read the thought-provoking review article by Slingo and Slingo on the impact of volatile anaesthetic gas emissions on climate change [1]. They have suggested that only desflurane will have full radiative effect on global warming due to its longer atmospheric lifetime of 14 years compared with others. Another key message for us practitioners is the caution against the simplistic use of global warming potential (GWP) to calculate the carbon footprint of volatile agents. While many, including us, have used GWP in the past, we agree with their call for caution in translating GWP to miles driven. Yet, we contend that, even if our contribution to greenhouse gases is not significant, choosing volatile agents with shorter life times seems common sense and aligns with the recommendations of the AR-6 report of the Intergovernmental Panel on Climate Change to curb the use of near-term climate forcers. Additionally, such choices also align with the principles of ‘choosing wisely’ where resource consciousness is taken into consideration. We must refine our practice by both choosing the anaesthetic agent with the most favourable profile and the ideal way to use that agent. Environmental sustainability aside, there are a multitude of reasons to refrain from using desflurane, which is far from an ideal anaesthetic agent. First, it has lower potency requiring approximately three times more mass of agent for the same clinical effect compared with sevoflurane. Second, its pungency may cause bronchial reactivity, and increase cerebral blood flow and cerebral spinal fluid pressure, causing sympathetic stimulation [2]. These undesirable effects outweigh the marginal clinical benefit of earlier recovery [3]. With a better understanding of the pharmacokinetic properties of sevoflurane, adjustment of the sevoflurane dosage towards the end of surgery negates the latter perceived advantage. Third, the costs of desflurane remain significantly higher than sevoflurane, especially given its reduced potency. Institutions have demonstrated significant cost savings with reduced desflurane use [4]. Moreover, low fresh gas flows should be employed using automated fresh gas flow control, which is lacking in even highly resourced countries such as the USA, one of the highest emitters of healthcare-related greenhouse gases. Irrespective of our total contribution to climate change, the fact remains that desflurane is more harmful to the environment compared with sevoflurane. Desflurane has significantly higher atmospheric concentrations than sevoflurane and isoflurane, contributing 81% of the radiative forcing of the halogenated anaesthetic agents [5]. Taking the economic and perhaps environmental considerations, all healthcare sectors are beginning to remove desflurane from clinical practice. This is much needed considering the growing surgical burden and the future need to improve access to surgeries in low-income countries. We agree with the statement by Slingo and Slingo [1] that selecting a volatile agent should be based on clinical judgement; however, even with that in mind, desflurane is disadvantageous from a value-based perspective for both patients and institutions. Further, even if the impact of volatile agents on the environment may seem minimal, action is better than inaction, and small actions in the right direction are to be encouraged to combat the climate emergency. Choosing sevoflurane on the grounds of clinical, economic and environmental reasons aligns with the triple bottom line. Refining practice with automated fresh gas flow or closed circuits to minimise usage is necessary. There are many more strategies anaesthetists need to focus on for combating the climate crisis; choosing anaesthetic agents is only one component. We must keep moving and not get distracted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".