Mechanical Mapping of the Common Carotid Artery in Healthy Individuals Aged 2 to 40 Years
Bibliographic record
Abstract
(1) Background: In 2022, the World Stroke Organization said there were more than 12.2 million new cases of stroke each year, between all ages and sexes. Six and a half million people die each year from stroke. Ischemic stroke accounts for 7.6 million (62%) cases, with 3.3 million (51%) deaths. Stroke is mainly linked to the atherosclerosis of a large artery. (2) Objective: Since the carotid artery directly supplies the brain, we used age-dependent mechanical mapping on the healthy common carotid artery (CCA) with the aim of being able to predict and thus potentially prevent ischemic stroke. (3) Methods: We assessed the CCA stiffness of 95 healthy control (CTL) females (2.23–39.46 years) and 107 healthy CTL males (2.85–40 years). Cine-loops of B-mode CCA data were digitally recorded with conventional medical ultrasound devices. Arterial wall elastic moduli were estimated offline using a proprietary non-invasive imaging-based biomarker algorithm (ImBioMark). Statistical analyzes were carried out with Excel software. (4) Results: Females showed a linear regression profile of CCA elastic moduli ranging from 41 ± 2 kPa to 54 ± 17 kPa (R2 = 0.88), while males showed one ranging from 38 ± 5 kPa to 63 ± 22 kPa (R2 = 0.83). For qualitative and quantitative illustrations, the elastic modulus data of CTLs were compared with those of subjects with Kawasaki disease and subjects born prematurely, respectively. (5) Conclusions: This study introduced some fundamental features of the mechanical evolution of the CCA as a function of age (2–40 years). Since atherosclerotic arteriopathy starts early in life, this gives the ability to predict risks of stroke and other cardiovascular diseases with the possibility of applying a more comprehensive range of potential preventive measures early in life. This is consistent with preventive medicine objectives which aim to be more predictive to implement pre-emptive measures as opposed to diagnostic and curative approaches.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".