Derivation of a CT Angiography-Based Arch Atherosclerosis Grading in Cryptogenic Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: We aimed to develop a novel computed tomography angiography (CTA)-based grading system to quantify the severity of aortic arch disease and compare the detection of aortic arch atherosclerosis (AAAthero) on routinely acquired arch-to-vertex CTA against transesophageal echocardiogram (TEE) among patients with cryptogenic ischemic stroke. METHODS: A systematic literature review was conducted to develop a computed tomography (CT)-based AAAthero grading system. CTA was compared against TEE for detecting AAAthero. The severity of arch atherosclerosis was scored based on a 5-point grading system. Patients with cryptogenic stroke who underwent both CTA and TEE were included in the derivation cohort to assess the sensitivity and specificity of CTA compared to TEE. The CT-based grading system for aortic plaques was then applied to an independent cohort of patients with cryptogenic stroke. RESULTS: Three studies were identified in a systematic review, and 141 patients were included in the derivation cohort. AAAthero was detected in 29 patients (20.6%) and 28 patients (19.9%) on TEE and CTA, respectively. The sensitivity of CTA to detect any atherosclerosis was 76%, which increased to 100% to detect moderate to severe disease. The specificity was 95% for any atherosclerosis and 100% for moderate to severe arch disease. Seven patients with AAAthero on TEE had normal CTA, but mild arch disease. Meanwhile, six patients with CTA and negative TEE had plaques on the arch's transverse segment. CONCLUSIONS: Routinely acquired arch-to-vertex CTA provides an accurate, noninvasive alternative to TEE for detecting AAAthero, especially in clinically relevant moderate to severe arch disease.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".