Resident-led action for greener operating rooms: a pilot using the national perioperative sustainability scorecard
Bibliographic record
Abstract
Background Environmental determinants such as air pollution and extreme heat contribute to approximately 13 million preventable deaths globally every year. Paradoxically, the health-care sector itself considerably affects planetary health, with operating rooms accounting for 10–20% of hospital-related greenhouse gas emissions. In response, the Sustainable Health Systems Community of Practice developed a sustainability scorecard, later adapted into the national Sustainable Perioperative Care Scorecard by the CASCADES initiative. This tool outlines 13 evidence-based targets to guide sustainable practices in perioperative care. This quality improvement pilot project aimed to show the utility of this national, freely available scorecard in a resident-led assessment of perioperative sustainability practices. Methods Residents partnered with staff surgeons to apply the 2023 CASCADES Sustainable Perioperative Care Scorecard across two major academic institutions. Assessments covered domains such as sustainability leadership, anaesthetic gas usage, reduction of low-value care, reusable instrument adoption, and waste segregation. Findings Both institutions showed strong engagement with resident-led evaluations. Scorecard results could distinguish differences and opportunities in practice between the two sites. Both hospitals scored well on elements relating to limiting low-value care, minimising intraoperative fresh gas flows, and implementing reusable anaesthesia equipment, and several opportunities for improvement were identified. Interpretation This pilot project illustrates the practicality of a national scorecard for evaluating perioperative sustainability and underscores the important role of residents in leading climate-conscious health-care improvements. Engaging trainees in structured assessments can accelerate institutional efforts towards more sustainable perioperative practices. Funding None.
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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 | 0.035 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".