CARING NATURE (ClimAte neutRal INitiatives for GrowiNg heAlTh and care Unmet REquirements) - Waste Management in Health Care
Bibliographic record
Abstract
Background: Each OR has the potential to produce up to 2300 kg of waste/year. A recent study from UK, USA and Canada documented that ORs were found to be up to 6 times more energy-consuming than other hospital departments, but in addition, ORs generate several types of waste and by-products, each requiring a different disposal.
 Aims: This project aims to provide a paradigmatic change in the life-cycle of waste produced in the OR.
 Methods: 1) We will provide an accurate measure of the total amount of waste produced in the OR, by monitoring activities in the OR for a 2-weeks period. 2) We will produce operational guidelines, based on 3 levels of interventions: A) Reuse; B) Rethink (raise awareness and promote education on health-care waste); and C) Reduce. 3) We will produce an organization model based on a new tool to discard products and implement their recycling. 4) We will provide the blueprint of reusable products, by scrutinizing the market of surgical devices and testing/comparing potential devices to those routinary employed. 5) Finally, we will divulgate a training package for all healthcare workers working in the OR focused on the management of hospital waste.
 Results: This project is part of the CARING NATURE initiative, submitted and funded in the HORIZON-HLTH-2023-CARE-04 call. The 36-months project will start on January 2024.
 Conclusions: This EU-funded project will add knowledge, implement management with new tools, address unmet needs, promote re-use, limit overuse, and disseminate guidelines to radically change OR management.
 
 
 Funding: This work has been supported by the CARING NATURE project that has received funding from the European Union’s Horizon Europe research and innovation programme under the Grant Agreement No. 101101322.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".