A Standardized Method for Estimating the Carbon Footprint of Disposable Minimally Invasive Surgical Devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 CO 2e 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 CO 2e /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 CO 2e /kg) identified. The estimated embodied CF of MISDs ranged from 0.07 to 3.3 kg CO 2e , 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 CO 2 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it