The direct environmental impact of hip arthroscopy for femoroacetabular impingement: a surgical waste audit of five cases
Bibliographic record
Abstract
Health care facilities produce significant waste (2200 kg/bed/year) creating 2% of greenhouse gas emissions and 1% total solid waste nationwide, with 20-70% of waste coming from operating rooms. We performed a waste audit of hip arthroscopy for femoroacetabular impingement (FAI) to understand its environmental impact and identify areas for greening practices. A waste audit of five hip arthroscopy procedures for FAI was performed. All waste was collected and separated into six waste streams in real time: (i) normal/landfill waste; (ii) recyclable cardboards and plastics; (iii) biohazard waste; (iv) sharp items; (v) linens and (vi) sterile wrapping. The surgical waste (except laundered linens) from five FAI surgeries totaled 47.4 kg, including 21.7 kg (45.7%) of biohazard waste, 11.7 kg (24.6%) of sterile wrap, 6.4 kg (13.5%) of normal/landfill waste, 6.4 kg (13.5%) of recyclable plastics and 1.2 kg (2.6%) of sharp items. An average of 9.4 kg (excluding laundered linens) of waste was produced per procedure. Given the considerable biohazard waste produced by FAI procedures, additional recycling programs, continued adherence to proper waste segregation and an emphasis on 'green outcomes' is encouraged to demonstrate environmental responsibility and effectively manage and allocate finite resources.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".