Remnant Cholesterol and Carotid Intraplaque Neovascularization Assessed by Contrast-Enhanced Ultrasonography in Patients With Ischemic Stroke
Bibliographic record
Abstract
Background: We investigated the relationship between remnant cholesterol and carotid intraplaque neovascularization (IPN) assessed by contrast-enhanced ultrasonography (CEUS) in patients with ischemic stroke. Methods: This was a single-center study. Remnant cholesterol is calculated as total cholesterol minus low-density lipoprotein cholesterol (LDL-C) minus high-density lipoprotein cholesterol (HDL-C). All patients underwent CEUS. IPN is graded according to the presence and location of microbubbles within each plaque. Results: The cohort included 110 patients with ischemic stroke. Patients with an IPN grading of 2 had higher triglyceride (TG), non-HDL-C, and remnant cholesterol concentrations than those with an IPN grading of < 2 (TG: 1.45 ± 0.69 vs. 0.96 ± 0.24 mmol/L, P < 0.001; non-HDL-C: 2.63 ± 0.85 vs. 2.31 ± 0.64 mmol/L, P = 0.037; remnant cholesterol: 0.57 ± 0.23 vs. 0.44 ± 0.07 mmol/L, P < 0.001). The multivariate-adjusted odds ratio (95% confidence interval) for remnant cholesterol was 27.728 (2.714 - 283.253) for an IPN grading of 2 in the subset of patients with an optimal LDL-C concentration. Conclusions: The remnant cholesterol concentration is significantly associated with carotid IPN on CEUS in patients with ischemic stroke with an optimal LDL-C concentration. Remnant cholesterol may be an important indicator of risk stratification in patients with ischemic stroke.
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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) | 0.001 | 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".