Coronary Artery Vasculitis and Encasement: Multimodality Imaging Findings and Mimics
Bibliographic record
Abstract
Coronary artery vasculitis (CAV) and coronary artery encasement are rarely diagnosed conditions that are important diagnostic considerations, particularly in patients with acute coronary syndrome without traditional cardiovascular risk factors or systemic illness. Vasculitis refers to inflammation of the blood vessel walls, which can be primary or secondary. This process should be distinguished from neoplastic involvement of the coronary arteries, termed coronary artery encasement. Prospective diagnosis of these diseases is challenging, often requiring multidisciplinary workup with careful attention to clinical presentation and multiorgan findings. While CAV and coronary artery encasement can be indistinguishable at coronary CT angiography, certain imaging features help order the differential diagnosis. CAV should be considered when there is smooth wall thickening that is circumferential and/or continuous. A diagnosis of coronary artery encasement is favored when there is irregular or nodular wall thickening that is eccentric to the vessel lumen. Epicardial fat stranding may also appear more extensive compared with CAV. Potential mimics of CAV include atherosclerosis, acute plaque rupture, coronary artery aneurysm, and spontaneous coronary artery dissection. Detection and diagnosis of CAV may help avoid complications related to accelerated atherosclerosis and infarction. Radiologists should be familiar with the range of pathologic conditions that can affect the coronary arteries beyond atherosclerosis as they may be the first to raise such diagnostic possibilities, guiding next steps in patient workup and management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| 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 | 0.001 |
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".