Contrast Enhanced Ultrasound: Visualization of Far Field and Classification of Plaque in the Extracranial Carotid Arteries
Bibliographic record
Abstract
Ultrasound stratification for the degree of carotid artery disease based solely on lumen reduction has poorly predicted patient outcomes. This pilot study focused on patients with moderate carotid artery stenosis. Our purpose was to use contrast imaging with ultrasound to improve carotid field. A total of 10 patients diagnosed with moderate carotid artery stenosis were rescanned with an administration of a contrast imaging agent. Two-dimensional (2D) imaging, color, and Doppler were utilized to scan the patients. The 20 carotid arteries were blindly read by 2 experienced physicians. Visualization of far field, quality of Doppler envelope, plaque morphology, and overall image quality were semi-quantifiably assessed. With the use of a contrast imaging agent, there was a reduction in interphysician interpretation variability. The Kappa coefficient yielded an increase in agreement for postcontrast imaging in the majority of variables. The Doppler envelope showed improvement from precontrast (0.06) to postcontrast (0.63). The visualization of the far fields demonstrated a significant increase in agreement (0.77, 0.71, and 0.67) postcontrast. Plaque morphology demonstrated enhancement in characterization with contrast (–0.09 to 0.66). In this study, contrast-enhanced ultrasound (CEUS) was found to increase overall image quality. Improved interpretation can enhance risk stratification and with further exploration could be used to guide treatment plans for patients with asymptomatic moderate carotid artery disease.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".