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).
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 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".