Low-dose cardiac imaging: reducing exposure but not accuracy
Bibliographic record
Abstract
Cardiac imaging techniques that use ionizing radiation have become an integral part of current cardiology practice. However, concern has arisen that ionizing radiation exposure, even at the low levels used for medical imaging, is associated with the risk of cancer. From a single diagnostic cardiac imaging procedure, such risks are low. On a population basis, however, malignancies become more likely on account of stochastic effects being more probable as the number of procedures performed increases. In light of this, and owing to professional and industrial commitment to the as low as reasonably achievable (ALARA) principle, over the last decade major strides have been made to reduce radiation dose in cardiac imaging. Dose-reduction strategies have been most pronounced in cardiac computed tomography. This was important since computed tomography has rapidly become a widely used diagnostic alternative to invasive coronary angiography, and initial protocols were associated with relatively high radiation exposures. Advances have also been made in nuclear cardiology and in invasive coronary angiography, and these reductions in patient exposure have all been achieved with maintenance of image quality and accuracy. Improvements in imaging camera technology, image acquisition protocols and image processing have lead to reductions in patient radiation exposure without compromising imaging diagnostic accuracy.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".