Empowering Surgeons, Anesthesiologists, and Obstetricians to Incorporate Environmental Sustainability in the Operating Room
Bibliographic record
Abstract
OBJECTIVE: We review the existing research on environmentally sustainable surgical practices to enable SAO to advocate for improved environmental sustainability in operating rooms across the country. SUMMARY OF BACKGROUND DATA: Climate change refers to the impact of greenhouse gases emitted as a byproduct of human activities, trapped within our atmosphere and resulting in hotter and more variable climate patterns.1 As of 2013, the US healthcare industry was responsible for 9.8% of the country's emissions2; if it were itself a nation, US healthcare would rank 13th globally in emissions.3 As one of the most energy-intensive and wasteful areas of the hospital, ORs drive this trend. ORs are 3 to 6 times more energy intensive than clinical wards.4 Further, ORs and labor/delivery suites produce 50%-70% of waste across the hospital.5,6 Due to the adverse health impacts of climate change, the Lancet Climate Change Commission (2009) declared climate change "the biggest global health threat of the 21st century" and predicted it would exacerbate existing health disparities for minority groups, children and low socioeconomic patients.7. METHODS/RESULTS: We provide a comprehensive narrative review of published efforts to improve environmental sustainability in the OR while simultaneously achieving cost-savings, and highlight resources for clinicians interested in pursuing this work. CONCLUSION: Climate change adversely impacts patient health, and disproportionately impacts the most vulnerable patients. SAO contribute to the problem through their resource-intensive work in the OR and are uniquely positioned to lead efforts to improve the environmental sustainability of the OR.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".