Scope sustainability: slashing single-use plastic bottles
Bibliographic record
Abstract
Background The health-care sector is a substantial contributor to greenhouse gas emissions, with single-use plastic sterile water bottles generating considerable waste. To address this issue, our endoscopy programme implemented a multistep quality improvement initiative replacing sterile water in disposable plastic bottles with tap water in reusable containers for upper and lower endoscopic procedures, excluding endoscopic retrograde cholangiopancreatography. Methods We first performed a scoping review that identified no clinical difference between sterile water and tap water in upper and lower endoscopic procedures, highlighting both economic and environmental benefits. A multidisciplinary team, including representatives from infection prevention, infectious diseases, hospital facilities, patient safety, ethics, and endoscopy quality improvement, collaborated to ensure safety and feasibility of using tap water. Institutional approval was obtained, and implementation is being evaluated using an interrupted time series analysis, tracking complications at 1 week and 30 days post procedure. Ongoing surveys collect feedback to refine the process. Findings Each 1 L sterile water bottle generates approximately 0·575 kg CO 2 and contains 240 000 microplastics and nanoplastics. Within the first 2 weeks of using tap water, usage of 480 bottles was discontinued, reducing CO 2 emissions by 276 kg and preventing the release of 115 million microplastics and nanoplastics. Annual cost savings from all three hospital sites are projected at CAD 47 145, based on eliminating 15 715 bottles used for colonoscopies. No increase in complications has been observed. Interpretation This initiative effectively reduced plastic waste and emissions while maintaining patient safety. It provides a replicable model for sustainable health-care practices, promoting environmental stewardship and mitigating potential health risks from microplastic exposure. Funding None.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".