<i>Radiology: Cardiothoracic Imaging</i> Highlights 2022
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
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
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) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it