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Record W4406821032 · doi:10.1016/j.gastha.2025.100625

Ripple Effect: Safety, Cost, and Environmental Concerns of Using Sterile Water in Endoscopy

2025· review· en· W4406821032 on OpenAlexaff
Deepak Agrawal, Seth D. Crockett, Sonali Palchaudhuri, Lyndon V. Hernandez, Kevin Skole, Rahul A. Shimpi, Jim Collins, Daniel von Renteln, Heiko Pohl

Bibliographic record

VenueGastro Hep Advances · 2025
Typereview
Languageen
FieldImmunology and Microbiology
TopicMedical Device Sterilization and Disinfection
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsEndoscopyRippleSterile waterEnvironmental scienceMedicineBusinessOperations managementSurgeryEngineeringPulp and paper industryElectrical engineering

Abstract

fetched live from OpenAlex

The gastroenterology societies are committed to reducing the carbon footprint of endoscopies and hence, re-examining waste-generating practices. One such practice is the recommendation to use sterile water during endoscopy for endoscopy lens cleaning and colon irrigation. We critically reviewed all published medical literature and guidelines on the safety of the type of water used in endoscopy. We calculated the cradle-to-grave carbon footprint of a 1-L sterile water bottle and compared it to published studies on bottled drinking water. Guidelines recommending sterile water during endoscopy are based on limited evidence and mostly expert opinions. Referenced studies utilize care protocols that are not practiced. There is also considerable cross-referencing of review articles and guidelines. Two clinical studies directly comparing tap and sterile water in gastrointestinal endoscopy found tap water to be a safe and practical cost-saving alternative to sterile water. The calculated carbon footprint of bottled sterile water is 575 g CO<sub>2</sub> equivalent. No direct evidence supports the recommendation and widespread use of sterile water during gastrointestinal endoscopy procedures. It contributes to health-care waste and climate change and is costly. We recommend tap water be used to fill sterile water bottles until evidence shows the need for alternative practice. It would be prudent to re-evaluate guidelines and write new ones that consider harm to the environment and society in the provision of care to patients, especially when the intervention may be more harmful than the risk it aims to address.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.318
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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