Po‐Thur Eve General‐24: Non‐Invasive Imaging Phenotypes of Carotid Atherosclerosis in Subjects: MRI, B‐mode and 3D Ultrasound Measurements
Bibliographic record
Abstract
Atherosclerosis is a chronic inflammatory disease, characterized by the accumulation of lipids and fibrous elements within the inner‐most layer of the artery wall. Vulnerable atherosclerotic plaques may eventually rupture, resulting in emboli that can obstruct blood flow and result in stroke or myocardial infarction. Direct measurements of atherosclerosis include non‐invasive imaging phenotypes such as MRI and 3DUS derived plaque and wall volume measurements and B‐mode US measurements of the intima media thickness (IMT). Here we report the first comparison of IMT with 3D Ultrasound and MRI‐derived atherosclerosis phenotypes measured in five subjects with moderate carotid atherosclerosis. Five research subjects with carotid plaque area ⩾ 0.5 cm2 were studied and all subjects were undergoing treatment for hyperlipidemia. Mean age was 66 yrs with no significant difference between males and females. Subjects underwent MRI, 3DUS and 2D B‐mode US of the left and right carotid arteries. A single observer carried out 3DUS vessel wall volume measurements (VWV) in 10 subject images at both time points, a second observer measured MRI vessel wall volume; a third observer measured IMT. Both 3DUS VWV and MRI VWV measurements were repeated five times to determine intra‐observer variability and variability between time points. At baseline 3DUS and MRI VWV measurements were not significantly different. No significant differences was observed for mean 3DUS VWV and MRI VWV at test and retest. No significant difference between mean IMT at test and retest was found. IMT measurements had the highest intra‐class correlation coefficients and the lowest coefficient of variation values.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".