Carbon Footprint Reduction Associated With Multidisciplinary Pediatric Airway Clinics: A Program Evaluation Study
Bibliographic record
Abstract
Objective: Health care is a significant contributor to the climate crisis. Multidisciplinary clinics (MDC) may reduce carbon emissions by combining multiple appointments into one. This is the first program evaluation study to quantify the carbon footprint associated with multidisciplinary pediatric airway clinics. Study Design: Retrospective. Setting: Children's Hospital at London Health Sciences Center, London, Canada. Methods: Pediatric airway MDC allows patients to see otolaryngology and respirology in one appointment. The carbon and financial savings (Canadian Dollars) of all patients attending the MDC from January 1, 2018 to December 31, 2022 were calculated. Patient postal codes and institutional parking rates were inputted into the CASCADES carbon accounting tool. Total distance was divided into unsustainable (vehicles) and sustainable (transit, walking, cycling) transportation to calculate carbon emissions. Travel costs included cost/kilometer for vehicles (maintenance, license/registration, insurance, fuel) and costs/ride for transit. Results: A total of 560 MDC appointments for 300 patients saved 77,785 km. Total carbon emissions saved from travel averted was 16.21 tonnes. The total carbon emissions saved, minus public transit, was 15.60 tonnes. Using the Natural Resources Canada Greenhouse Gas Equivalencies Calculator, 16.21 tonnes are approximately equivalent to 5 passenger vehicles, 6906 L of gasoline, 3.8 homes' energy, and 10.8 homes' electricity use for one year, 36.6 barrels of oil consumed, and 675 propane cylinders. Travel costs of $28,891.83 (no parking), $30,519.40 ($4 minimum parking fee), or $33,774.55 ($12 maximum parking fee) were saved. Conclusion: MDC effectively reduced carbon emissions and offered patients financial savings. Similar models can be adapted across institutions to help mitigate climate change.
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 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".