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Record W4407285057 · doi:10.1093/jcag/gwae059.110

A110 SUSTAINABILITY IN EVERYDAY GI PRACTICE: QUANTIFYING TRAVEL RELATED CARBON EMISSIONS FOR IN-PERSON APPOINTMENTS AND MODELS FOR REDUCTION THROUGH USE OF TELEHEALTH

2025· article· en· W4407285057 on OpenAlexaffabout
Ciarán Galts, Sama Anvari, Grigorios I. Leontiadis

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTelehealthSustainabilityEnvironmental economicsReduction (mathematics)BusinessEnvironmental planningPsychologyTelemedicineEnvironmental scienceHealth careEconomic growthEconomics

Abstract

fetched live from OpenAlex

Abstract Background The health care industry alone contributes to approximately 5% of annual carbon emissions and gastroenterology represent a more resource intensive subspecialty so much so that multiple societies have begun initiatives with aims to reduce the environmental impact of gastroenterology practice. After COVID-19 there was a large adoption of telehealth across many disciplines including gastroenterology. This had the benefit of reducing viral transmission but also significantly reduced travel related emissions for routine appointments. Since that time there has been a varied continuation of virtual care. Aims We aimed to quantify the average carbon emissions associated with travel to non-endscopic gastroenterology appointments. We also aimed to develop models for emission reductions based on possible changes to practice (e.g. conversion of follow-up appointments to telehealth). Methods Over a 2-week period, we conducted a cross-sectional analysis evaluating carbon emissions associated with travel to gastroenterology appointments. The average number of appointments per day was determined and by using postal codes we were able to estimate travel distances for patients. Carbon emissions were based on these travel distances using standard estimates (including average emissions by car, percentage of patients using alternate transportation, non-tail pipe emissions) and we assessed various estimated of emissions related to telehealth. We then used variable practice models to determine the potential emissions reductions. Results We assessed 975 appointments, of which 71 were excluded (e.g. insufficient data, non-physician appointments), leaving 904 included appointments of which 75% were follow-up (678) and the remained were new consultations (226). Sixteen different gastroenterologists had an average of 22.7 patients per day. The mean return distance travelled per appointment was 57.3 km which translates to 14.9 kg CO2 per patient visit. An average day at in clinic would then equate to 337.3 kg CO2 per day, equivalent to 146.6 L gasoline or 15.5 trees’ annual carbon capture. By converting only appointments with a return distance over 100 km or follow-up appointments, we found that a 77% emissions reduction could be achieved. Conclusions There can be significant emissions savings with conversion of in-person visits to telehealth while still allowing for some visits to be in-person. Given the unique nature of gastroenterology requiring in-person visits for endoscopy, this may serve as one mechanism by which the collective group of Canadian gastroenterologists can substantilally reduce carbon emissions related to their practice. Impact on carbon emissions from conversion of in-person appointments to telehealth for gastroenterology Funding Agencies 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 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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.040
GPT teacher head0.290
Teacher spread0.249 · 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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Citations0
Published2025
Admission routes2
Has abstractyes

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