Comparison of B-Mode Ultrasound, 3-Dimensional Ultrasound, and Magnetic Resonance Imaging Measurements of Carotid Atherosclerosis
Bibliographic record
Abstract
OBJECTIVE: We compared the intraobserver and interscan variability of carotid atherosclerosis measured using B-mode ultrasound for quantifying intima media thickness (IMT), 3-dimensional ultrasound (3DUS) for quantifying vessel wall volume (VWV) and total plaque volume (TPV), and magnetic resonance imaging (MRI) for measuring VWV. We also evaluated the associations of these measurements and sample sizes required to detect specific changes in patients with moderate atherosclerosis. METHODS: Ten patients were evaluated with B-mode ultrasound, MRI, and 3DUS twice within 14 +/- 2 days. Measurements of IMT, MRI VWV, 3DUS VWV, and 3DUS TPV were performed by single observers using manual (VWV and TPV) and semiautomated (IMT) segmentation. RESULTS: Intraobserver coefficients of variation were 3.4% (IMT), 4.7% (3DUS VWV), 6.5% (MRI VWV), and 23.9% (3DUS TPV). Interscan coefficients of variation were 8.1% (MRI VWV), 8.9% (IMT), 13.5% (3DUS VWV), and 46.6% (3DUS TPV). Scan-rescan linear regressions were significant for 3DUS TPV (R(2) = 0.57), 3DUS VWV (R(2) = 0.59), and IMT (R(2) = 0.75) and significantly different (P < .05) for MRI VWV (R(2) = 0.87). CONCLUSIONS: B-mode ultrasound-derived IMT provided the highest intraobserver and interscan reproducibility. Three-dimensional measurements of VWV derived from 3DUS and MRI provided both high sensitivity and high intraobserver and interscan reliability.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".