To Dispose or to Reuse? Analyzing the Life Cycle Impacts and Costs of Disposal, Sterilization, and Reuse of Electrophysiological Catheters
Bibliographic record
Abstract
Given the growing ecological footprint of anthropomorphic activities, considering the environmental impacts of any process is becoming increasingly important. This is especially true for the healthcare industry, whose objective of maintaining human health standards is impeded by its own unsustainable practices. To this end, life cycle analysis is particularly helpful. There have not been many life cycle analyses performed on a healthcare device or on medical procedures. Many medical devices are single use, which leads to a significant waste management problem, particularly as plastic is widely used in their composition. The objective of this study is to present a life-cycle-thinking-based approach to compare the environmental impacts associated with single-use electrophysiological catheters with the sterilization of reusable electrophysiological catheters using hydrogen peroxide, ethylene oxide, and peracetic acid. A life cycle assessment was conducted considering different use, disinfection, and disposal scenarios for electrophysiological catheters, using ReCiPe midpoint and endpoint analysis with the SimaPro software. The findings indicate that using single-use disposable electrophysiological catheters, instead of sterilizing a single catheter using either ETO or hydrogen peroxide and reusing multiple times, is preferable from a purely environmental perspective. However, the costs reduce drastically when equipment is sterilized and reused instead of disposing them after using one time. This in turn illustrates that depending on the process, sanitizing and reusing medical devices may not always be more resource-efficient than single device usage. From a cost perspective, ETO sterilization has the lowest costs, and yet it leads to an aggregate environmental impact of over 20 times compared to the single-use scenario, mainly due to the required detoxification process. The outcomes of this research will assist the health care industry in identifying the most suitable operational procedures considering patient safety, economics, and environmental stewardship, and in developing policies and guidelines for a more sustainable healthcare sector.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".