Carotid intraplaque neovascularization predicts coronary artery disease and cardiovascular events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS: It is thought that the majority of cardiovascular (CV) events are caused by vulnerable plaque. Such lesions are rupture prone, in part due to neovascularization. It is postulated that plaque vulnerability may be a systemic process and that vulnerable lesions may co-exist at multiple sites in the vascular bed. This study sought to examine whether carotid plaque vulnerability, characterized by contrast-enhanced ultrasound (CEUS)-assessed intraplaque neovascularization (IPN), was associated with significant coronary artery disease (CAD) and future CV events. METHODS AND RESULTS: We investigated carotid IPN using carotid CEUS in 459 consecutive stable patients referred for coronary angiography. IPN was graded based on the presence and location of microbubbles within each plaque (0, not visible; 1, peri-adventitial; and 2, plaque core). The grades of each plaque were averaged to obtain an overall score per patient. Coronary plaque severity and complexity was also determined angiographically. Patients were followed for 30 days following their angiogram. This study found that a higher CEUS-assessed carotid IPN score was associated with significant CAD (≥50% stenosis) (1.8 ± 0.4 vs. 0.5 ± 0.6, P < 0.0001) and greater complexity of coronary lesions (1.7 ± 0.5 vs. 1.3 ± 0.8, P < 0.0001). Furthermore, an IPN score ≥1.25 could predict significant CAD with a high sensitivity (92%) and specificity (89%). The Kaplan-Meier analysis demonstrated a significantly higher proportion of participants having CV events with an IPN score ≥1.25 (P = 0.004). CONCLUSION: Carotid plaque neovascularization was found to be predictive of significant and complex CAD and future CV events. CEUS-assessed carotid IPN is a clinically useful tool for CV risk stratification in high-risk cardiac patients.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it