Estimating the environmental impact of disposable endoscopic equipment and endoscopes
Bibliographic record
Abstract
OBJECTIVE: Procedure-intense specialties, such as surgery or endoscopy, are a major contributor to the impact of the healthcare sector on the environment. We aimed to measure the amount of waste generated during endoscopic procedures and to understand the impact on waste of changing from reusable to single use endoscopes in the USA. DESIGN: We conducted a 5-day audit (cross-sectional study) of all endoscopies performed at two US academic medical centres with low and a high endoscopy volume (2000 and 13 000 procedures annually, respectively). We calculated the average disposable waste (excluding waste from reprocessing) generated during one endoscopic procedure to estimate waste of all endoscopic procedures generated in the USA annually (18 million). We further estimated the impact of changing from reusable to single-use endoscopes taking reprocessing waste into account. RESULTS: 278 endoscopies were performed for 243 patients. Each endoscopy generated 2.1 kg of disposable waste (46 L volume). 64% of waste was going to the landfill, 28% represented biohazard waste and 9% was recycled. The estimated total waste generated during all endoscopic procedures performed in the USA annually would weigh 38 000 metric tons (equivalent of 25 000 passenger cars) and cover 117 soccer fields to 1 m depth. If all endoscopic procedures were performed with single-use endoscopes and accounting for reprocessing, the net waste mass would increase by 40%. Excluding waste from ancillary supplies, net waste generated from reprocessing and endoscope disposal would quadruple with only using single-use endoscopes. CONCLUSION: This quantitative assessment of the environmental impact of endoscopic procedures highlights that a large amount of waste is generated from disposable instruments. Transitioning to single-use endoscopes may reduce reprocessing waste but would increase net waste.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".