Planetary Health and Climate Action in Radiology
Bibliographic record
Abstract
Climate change, biodiversity loss, and pollution are disrupting earth's biophysical systems, with adverse effects on local and global human health. Planetary health describes the inextricable link between human health and the health of earth's biophysical systems. There is urgent need for a stronger focus on planetary health among healthcare systems and radiology departments. Medical imaging is a substantial contributor to climate change, responsible for 0.8% to 1% of global greenhouse gas emissions. As demands for medical imaging continue to grow, so will the need for radiologists to provide leadership in environmentally sustainable medical imaging. Mitigation strategies targeting overall reductions in environmental impact are pivotal including reducing the energy consumption of medical imaging equipment and establishing a circular supply chain to reduce unnecessary waste. In addition, radiology departments will need to focus on adaptative measures which build resiliency to the impacts of climate change, some of which will be unavoidable. This review aims to define planetary healthcare in the context of radiology and provide a framework within which to consider specific actions to reduce the environmental footprint of medically necessary medical imaging.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".