Carbon savings potential of virtual care in obstructive sleep apnea and otitis media with effusion
Bibliographic record
Abstract
Abstract Objective To determine the carbon savings potential of incorporating virtual care into surgical care pathways for pediatric patients with obstructive sleep apnea or otitis media with effusion. Methods Pediatric patients with obstructive sleep apnea or otitis media with effusion were not enrolled, instead, a modeling cohort study design was used. This study utilized the British Columbia healthcare system and geography to model emissions. Care pathways were developed for pediatric patients with obstructive sleep apnea or otitis media with effusion requiring care at a tertiary pediatric center. Home addresses were located at the geographical center of the two most populated municipalities within each of the 10 most populated regional districts in 2020. Virtual visits replaced up to three clinically equivalent in‐person visits. Emissions (kgCO2e) for transport and virtual visits were estimated. Population‐weighted means and descriptive statistics were calculated. Results Utilizing 1, 2, or 3 virtual visits in the obstructive sleep apnea care pathway yielded potential emissions savings of 19.9%, 39.9%, and 59.8% respectively. Integrating 1, 2, or 3 virtual visits into the otitis media with effusion care pathway produced potential emissions savings of 16.6%, 33.2%, and 49.7%, respectively. Integrating 3 virtual visits can save up to 2156.8 kgCO2e per patient. Conclusions Appropriately conducting up to 50% of clinical encounters virtually for children with obstructive sleep apnea or otitis media with effusion reduced theoretical carbon emissions. For a single child, emission savings could reach over 2150 kgCO2e. Level of Evidence Level 5.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".