Surgical waste audit of 5 total knee arthroplasties
Bibliographic record
Abstract
BACKGROUND: Operating rooms (ORs) are estimated to generate up to one-third of hospital waste. At the London Health Sciences Centre, prosthetics and implants represent 17% of the institution's ecological footprint. To investigate waste production associated with total knee arthroplasties (TKAs), we performed a surgical waste audit to gauge the environmental impact of this procedure and generate strategies to improve waste management. METHODS: We conducted a waste audit of 5 primary TKAs performed by a single surgeon in February 2010. Waste was categorized into 6 streams: regular solid waste, recyclable plastics, biohazard waste, laundered linens, sharps and blue sterile wrap. Volume and weight of each stream was quantified. We used Canadian Joint Replacement Registry data (2008-2009) to estimate annual weight and volume totals of waste from all TKAs performed in Canada. RESULTS: The average surgical waste (excluding laundered linens) per TKA was 13.3 kg, of which 8.6 kg (64.5%) was normal solid waste, 2.5 kg (19.2%) was biohazard waste, 1.6 kg (12.1%) was blue sterile wrap, 0.3 kg (2.2%) was recyclables and 0.3 kg (2.2%) was sharps. Plastic wrappers, disposable surgical linens and personal protective equipment contributed considerably to total waste. We estimated that landfill waste from all 47 429 TKAs performed in Canada in 2008-2009 was 407 889 kg by weight and 15 272 m3 by volume. CONCLUSION: Total knee arthroplasties produce substantial amounts of surgical waste. Environmentally friendly surgical products and waste management strategies may allow ORs to reduce the negative impacts of waste production without compromising patient care. LEVEL OF EVIDENCE: Level IV, case series.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".