Physician and endoscopy nurse perspectives on tap water for gastrointestinal endoscopy: a cross-sectional survey on support, perceived barriers, and intent to advocate
Bibliographic record
Abstract
Abstract Background The standard water used for endoscopic irrigation is sterile water. Minimal evidence exists regarding sterile water use where there is access to clean water. The WHO has declared the climate crisis as the greatest global health crisis today; we must re-examine our practices and adapt them to promote environmental stewardship while maintaining safety. Method and objective We surveyed physicians and endoscopy nurses to determine their attitudes toward tap water use for irrigation in gastrointestinal endoscopic procedures. Results There were 88 complete responses collected from June to November 2024. The majority of respondents and endoscopy-performing consultants expressed comfort with tap water use (59 and 84%, respectively), perceived viability (62 and 68%, respectively), and an interest to implement (73 and 94%, respectively); however, discussions on the topic remained infrequent (77 and 81%, respectively). 82% of overall respondents and 93% of consultants were aware of potential cost-savings, with 69% and 87% more willing to consider tap water based on this. Respondents (60%) and consultants (73%) agree there is a lack of guidelines regarding tap water use and feel that policy barriers will hinder change (59 and 73% respectively). Overall, 59% of respondents and 73% of consultants are likely to advocate for change. Conclusion The majority of respondents view tap water as a viable, cost-effective alternative with environmental benefits. A strong intention to advocate for change highlights the presence of potential leaders in this space. By promoting and supporting these leaders through education and institutional change, a more sustainable future for endoscopy exists.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".