EP-269 An Audit of Carbon Emissions Generated by Virtual and In-Person Clinic Appointments During The COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Background/Introduction Virtual appointments have been considered in our department for many years, as a strategy to lower carbon emissions. The advent of COVID-19 prompted urgent implementation as in person appointments were limited. We performed a prospective audit to assess the effectiveness of this approach in lowering carbon footprint. Method Audit study of all surgical clinic appointments from 18/03/20–31/03/21at the Upper River Valley Hospital, New Brunswick. Mileage calculated based on a round trip from patient postcode to hospital address. CO2 / CO2 equivalents (CO2-e) emitted calculated from previously published data - mobile phone CO2-e; 0.0092751142 g/minute, laptop; 0.269216134 g/minute, standard car emissions 128.002 g/km. Results were analysed statistically. Results Discussion Implementation of virtual appointments significantly lowered the carbon footprint of our surgery clinic. This has the potential to be a positive development in the efforts against climate change. Factors such as quality assurance, patient and physician satisfaction need to be determined however.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".