A Standardized Method for Estimating the Carbon Footprint of Disposable Minimally Invasive Surgical Devices
Bibliographic record
Abstract
Objective: To propose a standardized methodology for estimating the embodied carbon footprint (CF) of disposable minimally-invasive surgical devices (MISDs) and their application in new benign prostatic hyperplasia (BPH) MISDs. Summary of Background Data: The estimation of the CO2e emissions of disposable surgical devices is central to empowering the healthcare supply chain. Methods: The proposed methodology relied on a partial product lifecycle assessment and was restricted to a specific part of scope 3, which comprised the manufacturing of surgical device- and non–device-associated products (NDAPs), including packaging and user manual. The process-sum inventory method was used, which involves collecting data on all the component processes underpinning disposable MISDs. The seven latest disposable MISDs used worldwide for transurethral prostatic surgery were dismantled, and each piece was categorized, sorted into the appropriate raw material group, and weighed. The CF was estimated according to the following formula: activity data (weight of raw material) × emission factors of the corresponding raw material (kg CO2e/kg). Results: The total weights of disposable packaging and user manuals ranged from 0.062 to 1.013 kg. Plastic was the most common and least emissive raw material (2.38 kg CO2e/kg) identified. The estimated embodied CF of MISDs ranged from 0.07 to 3.3 kg CO2e, of which 9% to 86% was attributed to NDAPs. Conclusions: This study described a simple and independent calculation method for estimating the embodied CF of MISDs. Using this method, our results showed a wide discrepancy in the estimated CO2 emissions of the most recent disposable MISDs for transurethral BPH surgery. Thus, the lack of CF information should be of major concern in the development of future MISDs.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".