The environmental burden of surgical waste from endovascular aortic aneurysm repair
Bibliographic record
Abstract
Background The health sector plays a significant role in contributing to climate change owing to its considerable carbon footprint. Operating rooms are responsible for producing up to one-third of the total waste generated by hospitals. To assess the environmental impact of endovascular aneurysm repair (EVAR), we conducted a surgical waste audit to evaluate waste production associated with this procedure. Methods We conducted a waste audit on 30 EVAR procedures performed by the vascular department. The waste was categorized into six streams: regular solid waste, recyclable plastics, recyclable paper, biohazard waste, laundered linens, and sharps. The volume and weight of each stream were measured and quantified. Using Canadian hospital discharge abstract data (2003-2016), we estimated the annual weight and volume totals of waste generated from all EVAR procedures performed in Ontario. Results The average surgical waste (excluding laundered linens) per EVAR was 20.72 kg, of which 13.3 kg (64.2%) was normal solid waste, 2.71 kg (12.8%) was biohazard waste, 2.44 kg (12%) was device boxes, 2.09 kg (10.1%) was recyclables, and 0.18 kg (0.9%) was sharps. The average volume of waste per EVAR was 0.77 m 3 . Device packaging made a significant contribution to the total waste. We estimated that landfill waste from the 19,219 elective EVAR procedures performed in Ontario between 2003 and 2016 amounted to 392,067 kg by weight and 14,798 m 3 by volume. Conclusions EVARs generate substantial surgical waste. By adopting environmentally friendly surgical products and implementing effective waste management policies, operating rooms could significantly reduce their environmental impact without compromising patient care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".