Designing Take-Back for Single Use Medical Devices: The Case of Returpen <sup>TM</sup>
Bibliographic record
Abstract
Focus on take-back of waste products is currently enjoying increased importance, as attention on environmental sustainability and circular economy grows. Single-Use or Disposable Medical Devices (SUDs), which in homecare settings often end up in landfills or incineration, are currently subject to attention, regarding the potential to slow the flow of waste and seek new value creation possibilities. Via a descriptive single-case study of the “Returpen TM” initiative—a collaborative take-back initiative launched in three municipalities in Denmark—characteristics are elicited, of the planning, launch, and implementation, of the first 6-month pilot of the Returpen TM initiative. Returpen TM is a collaborative partnership of 15 public and private organizations and is adopting an end-to-end approach for its development and execution, including numerous professional workstreams. The pilot of the Returpen TM achieved participation of 66 of the existing 73 pharmacies in 3 municipalities (90% participation rate), and an overall return rate of 13% for the used insulin pens, despite the limitations caused by the covid-19 pandemic. The return rates ranged from 10% to 15% in the 3 municipalities, and overall, the second quarter recovery (15%) was higher than the first quarter (11%). Returpen TM demonstrates how a workstream-based approach can provide a practical framework for the development and implementation of SUD take-back in a homecare setting. The case describes how the pharmaceutical industry is taking proactive measures to contribute to a more circular economy for disposable medical devices, including the infrastructure and ecosystem necessary to ensure a closed-loop system for medical devices.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".