Single sweep three-dimensional carotid ultrasound: Reproducibility in plaque and artery volume measurements
Bibliographic record
Abstract
BACKGROUND: There is a need for non-invasive and accurate techniques for assessment of severity of atherosclerotic disease in the carotid arteries. Recently an automated single sweep three-dimensional ultrasound (3D US) technique became available. The aims of this study were to evaluate the feasibility and reproducibility of the automated single sweep method in a cohort of patients undergoing clinically indicated carotid ultrasound. METHODS: Consecutive patients with a history of stroke or transient ischemic attack (TIA) and having a plaque in the internal carotid artery (ICA) were recruited for this study. Imaging was performed using a Philips iU 22 ultrasound system equipped with the single sweep volumetric transducer vL 13-5. Analysis was performed offline with software provided by the manufacturer. Two independent observers performed all measurements. RESULTS: Of 137 arteries studied (from 79 patients), plaque and artery volumes could be measured in 106 (77%). Reproducibility of plaque volume measurements was assessed in 82 arteries. Bland-Altman analysis demonstrated good inter-observer reproducibility with limits of agreement -0.06 to +0.07 ml. The mean percentage difference between two observers was 5.6% ± 6.0%. Reproducibility of artery volume measurement was assessed in 31 cases. Bland-Altman analysis demonstrated limits of agreement from -0.15 to +0.15 ml. The mean percentage difference was 6.4 ± 5.9%. CONCLUSION: The new automated single sweep 3D ultrasound is feasible in the majority of patients. Good reproducibility in plaque and artery volume measurements makes this technique suitable for serial assessment of carotid plaques.
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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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".