Current Biomarkers for Carotid Artery Stenosis: A Comprehensive Review of the Literature
Bibliographic record
Abstract
Carotid artery stenosis (CAS), an atherosclerotic disease of the carotid artery, is one of the leading causes of transient ischemic attacks (TIA) and cerebrovascular attacks (CVA). The atherogenic process of CAS affects a wide range of physiological processes, such as inflammation, endothelial cell function, smooth muscle cell migration and many more. The current gold-standard test for CAS is Doppler ultrasound; however, there is yet to be determined a strong, clinically validated biomarker in the blood that can diagnose patients with CAS and/or predict adverse outcomes in such patients. In this comprehensive literature review, we evaluated all of the current research on plasma and serum proteins that are current contenders for biomarkers for CAS. In this literature review, 36 proteins found as potential biomarkers for CAS were categorized in to the following nine categories based on protein function: (1) Inflammation and Immunity, (2) Lipid Metabolism, (3) Haemostasis, (4) Cardiovascular Markers, (5) Markers of Kidney Function, (6) Bone Health, (7) Cellular Structure, (8) Growth Factors, and (9) Hormones. This literature review is the most up-to-date and current comprehensive review of research on biomarkers of CAS, and the only review that demonstrated the several pathways that contribute to the initiation and progression of the disease. With this review, future studies can determine if any new markers, or a panel of the proteins explored in this study, may be contenders as diagnostic or prognostic markers for CAS.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".