Role of CT Angiographic Plaque Morphologic Characteristics in Addition to Stenosis in Predicting the Symptomatic Side in Carotid Artery Disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Atherosclerotic disease of the carotid artery is an important cause of ischemic stroke. We evaluated carotid plaque morphologic features by using CTA in addition to stenosis in the setting of symptomatic hemispheric TIA/stroke to identify factors that may predict plaque activity. MATERIALS AND METHODS: Six hundred seventy-three patients (408 men; ages, 18-91 years; mean, 65.8 +/- 15.2 years) presenting with hemispheric ischemic symptoms and having a CTA that included imaging of both carotid arteries within 24 hours were studied. Scans were interpreted for morphologic features, such as plaque length and width, attenuation, shape, surface, presence and degree of calcification, and ILT in addition to stenosis. RESULTS: Univariable analysis showed that carotid occlusions (P = .01, OR = 5.27), high-grade stenosis (70%-99%) (P = .06, OR = 1.8), and the presence of ILT (P = .01, OR = 4.33) were highly predictive of the symptomatic side. Smooth plaque (P = .01, OR = 0.73) and extensive calcification (P = .03, OR = 0.72) were more commonly associated with the asymptomatic side. There was no correlation between plaque hypoattenuation (P = .7, OR = 1.06) or ulcerated plaque (P = .74, OR = 0.955) in predicting the symptomatic side. In a multivariable logistic regression model, the presence of ILT was still found to be significantly associated with the symptomatic side (P = .048, OR = 3.1) and the presence of extensive calcification, with the asymptomatic carotid artery (P = .047, OR = 0.69). CONCLUSIONS: In addition to higher stenosis grades, the presence of ILT is highly predictive of the symptomatic side in carotid disease. Smooth plaque and extensive calcification seem to afford a protective effect. This information may be useful in radiologic risk stratification in carotid disease in addition to the current evidence available based on stenosis criteria alone.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".