Cardiovascular Imaging, Climate Change, and Environmental Sustainability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Environmental exposures including poor air quality and extreme temperatures are exacerbated by climate change and are associated with adverse cardiovascular outcomes. Concomitantly, the delivery of health care generates substantial atmospheric greenhouse gas (GHG) emissions contributing to the climate crisis. Therefore, cardiac imaging teams must be aware not only of the adverse cardiovascular health effects of climate change, but also the downstream environmental ramifications of cardiovascular imaging. The purpose of this review is to highlight the impact of climate change on cardiovascular health, discuss the environmental impact of cardiovascular imaging, and describe opportunities to improve environmental sustainability of cardiac MRI, cardiac CT, echocardiography, cardiac nuclear imaging, and invasive cardiovascular imaging. Overarching strategies to improve environmental sustainability in cardiovascular imaging include prioritizing imaging tests with lower GHG emissions when more than one test is appropriate, reducing low-value imaging, and turning equipment off when not in use. Modality-specific opportunities include focused MRI protocols and low-field-strength applications, iodine contrast media recycling programs in cardiac CT, judicious use of US-enhancing agents in echocardiography, improved radiopharmaceutical procurement and waste management in nuclear cardiology, and use of reusable supplies in interventional suites. Finally, future directions and research are highlighted, including life cycle assessments over the lifespan of cardiac imaging equipment and the impact of artificial intelligence tools.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it