The green ICU: how to interpret green? A multiple perspective approach
Bibliographic record
Abstract
Mitigating environmental impacts is an urgent challenge supported by (scientific) intensive care societies worldwide. However, making green choices without compromising high-quality care for critically ill patients may be challenging. The current paper describes a three-step approach towards green intensive care units. Starting with the measurement of environmental sustainability, intensive care units can identify hotspots, quantify the environmental impacts of products and procedures, and monitor sustainable progress. Subsequently, a multidisciplinary approach is proposed to improve environmental sustainability, including a collaboration of procurement specialists and healthcare professionals, using co-creation and green teams as efficient grassroots change agents. A context-specific approach for enhancing sustainable healthcare practices is key in order to fit local regulatory requirements and create support of professionals. A final step is to share results and create momentum, including publishing initiatives and participating in online (inter)national networks. Based on the core sustainability principles, this three-step approach towards green ICUs provides a valuable tool to professionals worldwide to facilitate change towards environmentally responsible intensive care units.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| 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".