<i>Radiology: Cardiothoracic Imaging</i> Highlights 2022
Bibliographic record
Abstract
Since its inaugural issue in 2019, Radiology: Cardiothoracic Imaging has disseminated the latest scientific advances and technical developments in cardiac, vascular, and thoracic imaging. In this review, we highlight select articles published in this journal between October 2021 and October 2022. The scope of the review encompasses various aspects of coronary artery and congenital heart diseases, vascular diseases, thoracic imaging, and health services research. Key highlights include changes in the revised Coronary Artery Disease Reporting and Data System 2.0, the value of coronary CT angiography in informing prognosis and guiding treatment decisions, cardiac MRI findings after COVID-19 vaccination or infection, high-risk features at CT angiography to identify patients with aortic dissection at risk for late adverse events, and CT-guided fiducial marker placement for preoperative planning for pulmonary nodules. Ongoing research and future directions include photon-counting CT and artificial intelligence applications in cardiovascular imaging. Keywords: Pediatrics, CT Angiography, CT-Perfusion, CT–Spectral Imaging, MR Angiography, PET/CT, Transcatheter Aortic Valve Implantation/Replacement (TAVI/TAVR), Cardiac, Pulmonary, Vascular, Aorta, Coronary Arteries © RSNA, 2023
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.019 |
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".