Hemorrhagic Plaques in Mild Carotid Stenosis: The Risk of Stroke
Bibliographic record
Abstract
OBJECTIVES: Embolic ischemic strokes cause significant mortality and morbidity worldwide. It has been proposed that some of these strokes are due to unstable carotid plaques with intraplaque hemorrhage (IPH) but a low overall degree of stenosis. Our aim was to test a fat-saturated T1-weighted (T1WI) black-blood sequence on MRI for the detection of IPH in symptomatic individuals and to quantify the relation between IPH, severity of stenoses, and ischemic brain lesions. MATERIALS AND METHODS: Sixty-two patients were examined by 3T MRI. Sequences included brain diffusion-weighted imaging (DWI) as well as 3D turbo spin echo (TSE) fat-saturated black-blood T1 of the carotid bifurcations, to detect IPH as a focal intraplaque hyperintensity. Both carotid arteries were analyzed in each patient. The North American Symptomatic Carotid Endarterectomy Trial scale was used for quantification of stenosis degree. RESULTS: Thirty-six out of 62 patients (mean age, 74) showed brain ischemia on DWI. Fifteen of these 36 patients (42%) had associated ipsilateral IPH at the carotid bifurcation or the proximal internal carotid artery. Mean degree of stenosis in this group was 50%. In 21 patients with ischemia without IPH, the mean degree of stenosis was 44%. CONCLUSIONS: MRI with 3D TSE fat-saturated black-blood T1 technique is a safe, reliable, and noninvasive tool for the detection of IPH. A high percentage (42%) of ischemic events in patients with low- to moderate-degree stenosis were associated with IPH, an easily detectable imaging biomarker of plaque vulnerability. The ability to confirm IPH by MRI may help stratify patients into different risk and treatment groups in the future.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".