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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".