Sustainability in Surgery: Reusable vs Single-Use Surgical Equipment
Bibliographic record
Abstract
Ms. A is a 79 year old woman who slipped on her icy driveway and was rushed to the emergency department via ambulance at 9:30 AM. She presents with severe pain on the left hip, swelling, and an inability to bear weight on her left side. She has a history of osteoarthritis, osteoporosis, and is dependent on her cane for mobility. She is currently on alendronate 70 mg PO once weekly. The attending ED physician orders imaging, which reveals a left hip fracture. Consultation with orthopedic service determined that she will be needing a hip replacement and is scheduled for surgery the next day. During surgery, reusable gowns, surgical instruments and drapes were used as per the hospital’s new green initiative. The surgery was a success with no complications. Ms. A was stable post-op, and discharged home after three days. However, she returns to the emergency room five days later with swelling and tenderness around the surgical site. An investigation of her surgical wound revealed a MRSA infection that was tied to a string of similar cases after the implementation of the hospital’s new green initiative. In this paper, we explore the current climate of environmentally sustainable surgical equipment.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".