Abstract TMP46: Computed Tomography of Non-Stenotic Carotid Plaque in Patients with Embolic Stroke of Undetermined Source (ESUS)
Bibliographic record
Abstract
Introduction: In up to 25% of patients with an ischemic stroke, the standard investigations fail to identify a cause. Many of these cryptogenic strokes are thromboembolic, but without an established source, so called “embolic strokes of undetermined source” (ESUS). Non-stenotic carotid plaque is a potential source. Hypothesis: We hypothesized that among patients with ESUS, there is an association between non-stenotic carotid atherosclerotic plaque diagnosed using CT angiography and ipsilateral ischemic stroke. Methods: From a prospectively maintained stroke registry, we identified consecutive patients between January 2012 and March 2015 with a carotid territory ischemic stroke, who fulfilled the diagnostic criteria for ESUS. Two radiologists, blinded to clinical information, independently measured carotid plaque thickness ipsilateral and contralateral to the ischemic stroke using CT angiography. Results: Eighty-five of 1038 ischemic stroke patients were included in the analysis. There was no difference in the degree of carotid artery stenosis ipsilateral versus contralateral to ischemic stroke (median 0% versus 0%, p=0.98). There was a weak correlation between degree of carotid artery stenosis and carotid plaque thickness (R2 = 0.26, p<0.001). Plaque thickness ≥ 5 mm (9/85 versus 1/85, p=0.008), ≥ 4 mm (16/85 versus 4/85, p=0.002), and ≥ 3 mm (30/85 versus 13/85, p=0.001) were each more common ipsilateral than contralateral to ischemic stroke. Plaque thickness ≥ 2 mm had the same frequency ipsilateral and contralateral to ischemic stroke. Conclusions: Our data suggest that large, non-stenotic carotid plaque may be an important source of embolism in patients with ESUS. We evaluated non-stenotic plaque using routine CT angiography, a method that could be easily translated into clinical practice.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".