ENVIRONMENTAL IMPACT OF DISPOSABLE ENDOSCOPIC EQUIPMENT AND ENDOSCOPES - A VOLUMETRIC ANALYSIS
Bibliographic record
Abstract
Aims Health care’s climate footprint contributes 5% to Europe’s greenhouse gas emissions (8% in the US). We aimed to quantify the environmental impact of single use endoscopic devices and materials. Methods We documented the daily volume of disposable material used during endoscopic procedures at a small, medium and large size hospital. We estimated the volume of non-recycled waste (landfill) and calculated its volume per endoscopy, per annum for each center, and extrapolated it to the annual procedure volume in the US (20 Million), using low and high waste estimates as minimum and maximum ranges. Results The volume of endoscopic waste filled annually between 375 and 5151 trash bags (114 L) occupying 28 to 390 m 3 between the low and high-volume centers. Nationally, endoscopic procedures would produce 532918 m 3 waste per year in the US, which would fill 980 single-family houses. If all colonoscopies and ERCPs were performed with single use endoscopes, it would add 100682 m 3 , and fill 185 single-family houses per year (180 with colonoscopes, 5 with duodenoscopes). Combined the total disposable material would take up 633600 m 3 or fill 1165 single family houses (range 881 to 1578). Tab. 1 Waste generated during endoscopic procedures. Low volume center Medium volume center High volume center U.S. Endoscopies/year, n 1500 6000 13000 20 million Waste/procedure, mean 0.02 m 3 0.04 m 3 0.03 m 3 0.03 m 3 Waste/year 28 m 3 227 m 3 390 m 3 532918 m 3 Waste/year that would fill single-family houses, n (range) 0.1 0.4 0.7 980(696-1393) Conclusions Although limited, this is a first quantitative assessment of the environmental impact of single use material and devices used in endoscopy, showing large amount of waste produced annually. We need to better quantify the environmental impact of endoscopic procedures and waste, which will ultimately affect human health. Any cost-benefit analysis should also consider potential environmental harms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".