The carbon footprint of the vascular surgery operating room
Bibliographic record
Abstract
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.(JVS-Vascular Insights 2024;2:100151.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".