Quantification of carotid artery plaque and peri-vascular adipose tissue attenuation on computed tomography
Bibliographic record
Abstract
Abstract Aims Quantitative assessment of carotid artery plaque on computed tomography (CT) may identify high-risk phenotypes associated with culprit lesions and subsequent ischaemic stroke or transient ischaemic attack. Methods and results Carotid CT angiography was performed in 48 patients with acute ischaemic stroke or transient ischaemic attack within 21 days. Quantitative plaque assessment was performed in the proximal 6 cm of the internal and external carotid artery, distal 6 cm of the common carotid artery, and residual common carotid artery. Semi-automated quantification included assessment of non-calcified, calcified, low-attenuation, and total plaque, area and diameter stenosis, and peri-vascular adipose tissue attenuation. In 48 patients (mean age 71 ± 11 years, 67% male), 96 vessels were assessed with 30 (31%) identified as culprit vessels. Culprit internal carotid arteries had greater area [83 (65, 94) vs. 64 (55, 77)%] and diameter [56 (39, 74) vs. 32 (21, 48)%] stenosis and more non-calcified [563 (413, 965) vs. 428 (283 649) mm3, P = 0.04], low-attenuation [33.7 (6.9, 72.4) vs. 16.3 (3.35, 54.3) mm3, P = 0.01], and total [699 (455, 1057) vs. 492 (311, 809), P = 0.04] plaque. There was no difference in calcified plaque or peri-vascular adipose tissue attenuation between culprit and non-culprit internal carotid arteries. There were no differences in quantitative plaque or peri-vascular adipose tissue attenuation in the external carotid artery or common carotid artery. Conclusion Carotid atherosclerotic plaque characteristics are the principal features associated with culprit plaques with little or no demonstrable relationship with calcified plaque or increased peri-vascular adipose tissue attenuation.
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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 | 0.004 | 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".