Reducing the Preoperative Ecological Footprint in Otolaryngology
Bibliographic record
Abstract
OBJECTIVES: To (1) evaluate the potential for recycling uncontaminated preoperative waste and (2) identify recycling differences within otolaryngology-head and neck surgery subspecialties. STUDY DESIGN: Prospective study. SETTING: Three university-affiliated tertiary level hospitals. SUBJECTS: Otolaryngology-head and neck surgery operative procedures. METHODS: A total of 97 operative procedures were evaluated. Preoperative waste products were sorted into recyclable and nonrecyclable materials; intraoperative waste was weighed for volume but not sorted. The preoperative period was defined as the opening of the surgical supply cart for operating room preparation until procedure initiation. Mass and volume of each type of waste were recorded upon the conclusion of the case. RESULTS: Approximately 23.1% of total operative waste mass (36.7% by volume) was derived from the preoperative set-up, of which 89.7% was recyclable. Pediatric procedures produced the least recyclable material per operation as a proportion of total waste, which was statistically different than the 2 highest recyclable subspecialties, general and rhinology (P = .006); the remaining subspecialties did not statistically differ in proportion of recyclable material produced. CONCLUSION: This study identified a source of clean recyclable materials that could eliminate 21% of operating room waste mass.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".