The environmental effects of non-invasive cardiac imaging
Bibliographic record
Abstract
The healthcare sector is a major contributor to the universal climate footprint, of this a significant proportion is attributable to medical imaging and further to dedicated cardiac imaging. The increasing availability and utility of cardiac imaging techniques for prognosis, diagnosis and management raises concerns for the impact of these investigations on the environment. Our objective was to review the published literature assessing the environmental impact of non-invasive imaging modalities within cardiology, subsequently helping guide physicians toward a more sustainable approach to cardiac imaging and improved awareness of the environmental impact of healthcare within this field. We conducted a systematic review of studies measuring the environmental impact of non-invasive cardiac imaging. A total of 8 studies were included in the final analysis. Cardiac imaging has a significant environmental impact, which varies by modality: lowest for echocardiography and highest for MRI. As a whole this field represents a significant contributor to climate-related threats to human health, which we should strive toward harm minimisation. This may be mitigated through the conscious utilisation of energy consumption and contrast media, as well as healthcare worker education and quality improvement to guide imaging choice based on environmental impact alongside conventional determinants such as patient characteristics, clinical guidelines and cost (visual abstract).
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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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".