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 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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".