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Record W4408895400 · doi:10.1055/s-0045-1806604

Carbon footprint of routine endoscopic procedures – a comprehensive assessment in three US endoscopy units

2025· article· en· W4408895400 on OpenAlexaff
Desmond Leddin, Heiko Pohl

Bibliographic record

VenueEndoscopy · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineEndoscopyCarbon footprintFootprintGeneral surgerySurgeryArchaeology

Abstract

fetched live from OpenAlex

Aims GI endoscopy is a procedure intensive specialty with high utilization of single use instruments and supplies. However, there has been a lack of carbon footprint analyses that encompass all aspects of GI endoscopy care. Our goal was to perform a comprehensive assessment of the carbon footprint of routine colonoscopies and upper endoscopies (EGD). Methods We conducted a prospective review of endoscopic procedures performed during a one-week audit at three diverse United States (US) endoscopy units. Cradle-to-grave life cycle assessment (LCA) was applied to measure the global warming potential expressed as kgCO2e following ISO-1404 standards. We considered all aspects of pre, intra, and postprocedural care and established a detailed life cycle inventory of all instruments, supplies, and medications used (referred to as supplies). We further included emissions related to patient travel, endoscope reprocessing, energy needs, and heating ventilation and air conditioning (HVAC); the latter using a mathematical model that considered unit space, building specifications, temperature variations, and geographic location (New England with predominant fossil fuel energy source). Based on prior surgical LCAs we planned to audit a minimum of 10 colonoscopies and 10 EGDs at each unit. Environmental impact assessment was performed using the Ecoinvent database and the TRACI method. Our primary outcome of interest was the carbon footprint of performing a routine endoscopy. We further examined contribution of emission sources and compared colonoscopy and upper endoscopy emissions. Results A total of 131 routine endoscopies were audited (71 colonoscopies, 36 EGDs, and 24 colonoscopies/EGDs). 45.5% were performed with moderate sedation and 54.5% with monitored anesthesia care. The polyp detection rate was 60.2%, and 81.7% of the procedures used any ancillary equipment (snare, forceps, or hemoclip). The carbon footprint for performing any routine procedure was 47.8 kgCO2e (range 36.7-58.8), for a colonoscopy it was 48.8 kgCO2e (range 37.2-59.4), and for an EGD it was 48.1 kgCO2e (range 37.2-59.4). Among all routine procedures, 48.6% of the carbon footprint (23.3 kgCO2e) was related to energy and HVAC use, while patient travel (predominantly in rural locations) made up 24.4% (11.7 kgCO2e). Supplies represented only 21.0% (10.0 kgCO2e) and reprocessing 6.0% (2.9 kgCO2e). The most pronounced variation between endoscopy units was seen for energy and HVAC emissions ranging from 12.3 to 34.2 kgCO2e. Performing EGD and colonoscopy for a patient on the same day reduced the carbon footprint by 7.9%. Conclusions This comprehensive analysis examined the carbon footprint for performing routine endoscopies in US practice. Energy and HVAC use (scope 1 and 2 emissions) were the major contributor (49%), and supplies (scope 3 emissions) made up only 21%. While emissions related to supplies confirm findings of a recent French study (10.0 vs 9.0 kgCO2e), travel and energy/HVAC emissions will vary based on location. Detected variations between endoscopy units will serve to identify alternative endoscopy practices to lower emissions. Publication History Article published online: 27 March 2025 © 2025. European Society of Gastrointestinal Endoscopy. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.041
GPT teacher head0.337
Teacher spread0.296 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Published2025
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