Characterising high‐risk plaque on cardiac <scp>CT</scp>
Bibliographic record
Abstract
Coronary computed tomography angiography (CCTA) is a well-established and reliable non-invasive imaging modality that provides a comprehensive assessment of coronary artery anatomy and luminal stenosis due to atherosclerosis. Owing to advances in CCTA software and technology, the composition and morphology of coronary plaque can be accurately evaluated. Adverse features which identify plaque as being high-risk or 'vulnerable' can provide a personalised cardiovascular risk assessment over and above stenosis severity. High-risk plaque features on CCTA include spotty calcification, low attenuation plaque, positive remodelling and the napkin ring sign. However, it can be challenging to characterise high-risk plaque accurately on CCTA, and as such, education and experience are required. In this pictorial essay, a comprehensive visual guide to high-risk plaque features on CCTA is provided, with clear examples and challenging cases that highlight common pitfalls. It is important for expert readers to properly identify these features given their association with adverse outcomes and potential future implications on intensive goal-directed medical therapy.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".