Climate resilient and environmentally sustainable radiology: a framework for implementation
Bibliographic record
Abstract
Climate change adversely impacts human health and transformations in our approach to work are needed to build environmentally sustainable and climate resilient radiology systems. Radiology practices must reduce greenhouse gas emissions generated in the delivery of care while simultaneously building infrastructure and processes to anticipate, respond to, and recover from climate-related environmental events. The purpose of this review is to highlight the links between climate change, human health, and radiology; discuss mitigation, adaptation, and response approaches; describe opportunities to leverage existing knowledge such as pandemic planning and supply chain management; and develop a radiology resilience checklist to assess vulnerabilities and inform actions necessary to achieve environmentally sustainable and climate resilient practices. The proposed framework is based on 5 pillars of climate resilience capacity-threshold, coping, recovery, adaptive, and transformative. Key actions include increasing awareness of the health impacts of climate change, optimizing infrastructure, improving supply chain management, reducing energy use, and addressing health disparities through collaboration with stakeholders. These strategies are needed to reduce the environmental impact radiology service delivery, prepare for and minimize the effects of climate change on imaging departments, and build capacity to recover quickly from climate-related environmental impacts, ultimately improving planetary health and human well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".