A Medical Equipment Lifecycle Framework to Improve Healthcare Policy and Sustainability
Bibliographic record
Abstract
The healthcare sector is struggling to become more environmentally friendly compared to other industries, evidently seen by the contribution to global emissions. These struggles have elicited some research on sustainable methods regarding the lifecycle of medical devices. Indeed, the World Health Organization (WHO) encourages the reuse of equipment and ethical donations, namely for the sake of the environment and sustainable global health. However, there is a lack of synthesis–multiple greener alternatives to the current healthcare system are developing without a connection to each other, hindering an increase in sustainability. Thus, there is a lack of global organization and standardization in medical equipment lifecycles. Inspired by the findings and guidelines of the Safe and Sustainable Medical Equipment Supply Subgroup (SASMES) of the International Rotary Fellowship of Healthcare Professionals, we created the Re-processing Medical Equipment: Rotarian Research Group for the Environment (Re-MERGE) to expand on these challenges. Re-MERGE follows the life cycle of medical devices in the United States of America through its initial stages of classification and various regulatory pathways, the middle stage of post-market requirements, and the end stage of disposal or donation and reprocessing. Our findings indicate that current medical device end-stages are inefficient, damaging to the environment, and burdensome to donation recipients; however, existing processes can provide improvements to medical device end-stage methods by drastically reducing environmental damage, improving healthcare globally, and increasing sustainability in the field. We identify that more research is needed to connect the implications of different medical device end stages. Additionally, we encourage the findings to be implemented to create more sustainable, effective methods of medical device disposal, donation, and reprocessing.
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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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".