The Case: Problem Identification – Metals in The Orthopaedic OR
Bibliographic record
Abstract
The session on Environmental Stewardship began with Angela Scharfenberger introducing the topic and expressing her enthusiasm for learning more about it. After outlining the learning objectives, she welcomed three panelists, with Brenna Mattiello being the first speaker. Brenna, a second-year medical student at the University of Calgary, discussed the significant issue of surgical metal waste produced in operating rooms (OR). She highlighted the origins of the SWIM project—Surgical Waste The Impact of Metal—initiated by Dr. Marcia Clark, aiming to trace the lifecycle of surgical metal waste from usage in the OR to disposal in landfills. Brenna elaborated on the detrimental environmental impact of incinerating surgical metals, which often end up in soil and contribute to pollution and health hazards. Brenna presented a thorough literature review revealing that the existing process for managing surgical metal waste follows a linear economic model, in stark contrast to a sustainable circular economy approach. This linearity culminates in incineration at the Swan Hills Treatment Center, where remnants are disposed of into the soil, perpetuating an environmental burden. The presentation underscored how misconceptions around hazardous versus non-hazardous waste lead to misclassification and increased costs for waste management. Brenna outlined the project's methodology, involving interviews with key individuals at South Health Campus to gather insights on the waste's lifecycle. The findings emphasized the need for clear guidelines for waste disposal and opportunities for recovery and recycling of surgical metals, with the potential to repurpose materials for use in electric vehicle batteries. She concluded with a call to action for further research into the disposal pathways across different facilities to enhance environmental practices in healthcare.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 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".