Abstract WP177: Frequency of Carotid Plaque With High-Risk Features in Cryptogenic Stroke: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background and Purpose: An ipsilateral non-obstructive carotid atherosclerosis (NOCA), defined as carotid plaque with <50% stenosis, is identified in 25% of cryptogenic strokes and could represent an unrecognized source of athero-embolism. We aimed to summarize available data regarding the frequency of NOCA with high-risk features in patients with cryptogenic stroke. Methods: A search of Pubmed and Ovid-Embase identified observational studies reporting carotid plaque imaging features in patients with cryptogenic stroke, from inception to July 15, 2019. The prevalence of NOCA with high-risk features in ipsilateral and contralateral carotid was determined by pooling study-specific estimates using a random-effect meta-analysis. Results: Six prospective studies enrolling a total of 255 participants with unilateral ischemic stroke in the anterior circulation were included. Carotid arteries were imaged with ultrasound, computed tomography or MRI to identify echolucency, ulceration, intraplaque hemorrhage, thrombus, or thickness ≥ 3 mm. The pooled prevalence of NOCA with high-risk features in the ipsilateral carotid was 34.0% (95% CI: 25.7 – 42.9) compared to 7.3 % (95% CI: 0.8 – 18.1) in the contralateral carotid. The odds ratio of finding a plaque with high-risk features in the ipsilateral versus the contralateral carotid was 4.1 (95% CI: 2.0 – 8.7). Conclusion: Plaques with high-risk features are four times more prevalent in the ipsilateral compared to the contralateral carotid artery in patients with cryptogenic stroke, suggesting a relationship to stroke risk. These features may aid in etiologic classification of stroke and risk stratification for secondary prevention therapy.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".