Carotid Ultrasound Maximum Plaque Height–A Sensitive Imaging Biomarker for the Assessment of Significant Coronary Artery Disease
Bibliographic record
Abstract
OBJECTIVES: We investigated the use of carotid intima-media thickness and carotid plaque in predicting significant angiographic coronary stenosis. METHODS: Three hundred eighteen consecutive outpatients underwent angiography and carotid ultrasound on the same day. The extent of coronary stenosis was determined using an established scoring system. Mean far distal carotid intima-media thickness of the common carotid artery, maximum plaque height, and total plaque area in the bulbs were measured by ultrasound. Cutoff values were identified using a receiver operating characteristic curve for predicting and ruling out coronary artery disease. RESULTS: The mean ± SD carotid intima-media thickness (≥50% stenosis = 0.91 ± 0.23 mm, <50% stenosis = 0.82 ± 0.18 mm), maximum plaque height (≥50% stenosis = 2.64 ± 0.85 mm, <50% stenosis = 1.72 ± 1.04 mm), and total plaque area (≥50% stenosis = 39.1 ± 27.7 mm(2) , <50% stenosis = 22.2 ± 23.4 mm(2) ) were significantly higher in patients with coronary artery disease (P ≤ 0.001 for all three comparisons). Increased CIMT, plaque height, and area correlated with increased number of affected vessels. Plaque height had the best negative likelihood ratio for ruling out disease (0.15). The optimal threshold values for predicting coronary disease were 0.82 mm for carotid intima-media thickness, 1.54 mm for plaque height, and 25.6 mm(2) for total plaque area. CONCLUSION: Increased carotid intima-media thickness and plaque measurements are indicative of the presence of epicardial coronary stenosis. Plaque burden is a more sensitive imaging biomarker for ruling out significant coronary artery disease, including in younger individuals.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".