Sustainable practice in gastroenterology: travel-related CO2 emissions for gastroenterology clinic appointments in Canada
Bibliographic record
Abstract
Abstract Background Telemedicine is increasingly common in gastroenterology and may represent an opportunity for improving sustainability in medical care. The purpose of this study was to determine the carbon emissions related to travel for in-person gastroenterology clinic appointments. Methods We conducted a cross-sectional analysis evaluating carbon emissions associated with travel to gastroenterology appointments over a 2-week period. We determined the average number of appointments per day and used patient’s postal codes to estimate travel distances. We estimated carbon emissions based on these travel distances and completed sensitivity analyses to model methods for emissions reductions. Results We assessed 975 clinic appointments, of which 71 were excluded (eg, insufficient data, non-physician appointments), leaving 904 included appointments of which 75% were follow-up (678) and the remainder were new consultations (226). Sixteen different gastroenterologists had an average of 22.7 patients per clinic. 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 our clinic was equal to burning 146.6 L of gasoline or the annual carbon capture of 15.5 trees. By changing follow-up appointments or those with a travel distance over 100 km to telehealth, emissions were reduced by 77%. Conclusions We demonstrate that a relatively modest change in the number of in-person visits can save thousands of litres of gasoline emissions annually from each practicing clinician. While we cannot avoid emissions related to travel for procedure-based appointments, the use of telemedicine is one potential strategy to reduce healthcare-related emissions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".