Carotid artery non invasive elastography (NIVE) to detect early changes of cardiovascular diseases in overweight and obese children
Bibliographic record
Abstract
Increased arterial stiffness is one of the first signs of atherosclerosis. The objective of this study was to use non-invasive elastography (NIVE) to detect early changes in vascular biomechanics associated with obesity in children. The NIVE algorithm also measured the intimamedia thickness (IMT) for comparison. NIVE was applied in 120 children, 60 with elevated body mass index (BMI) (≥ 85thpercentile for age and sex) and 60 non-overweight (BMIthpercentile). Participants were randomly selected from a longitudinal cohort, evaluating consequences of obesity in healthy children with one obese parent. The carotid wall was automatically segmented and elastograms were computed to measure the cumulated axial strain (CAS), cumulated axial translation (CAT), and maximal shear strain (Max |SSE|); IMT was also computed from segmented contours. Elastogram features were compared between groups with multivariate analyses to control for age, sex, Tanner stage, blood pressure, and low-density lipoprotein cholesterol (LDL). After Bonferroni correction, CAT was significantly higher in the elevated BMI group (0.68 ± 0.24 mm vs. 0.52 ± 0.18 mm), p <; 0.001. CAS/CAT was significantly lower in the elevated BMI group (9.54 ± 4.8 %/mm vs. 13.34 ± 6.46 %/mm), p = 0.001; the lower CAS/CAT ratio suggests stiffer arteries with less deformation for a similar translation. Before Bonferroni correction, IMT was significantly higher in the elevated BMI group (0.36 ± 0.05 mm vs. 0.32 ± 0.05 mm), p = 0.013. IMT statistical difference was no longer significant after Bonferroni correction. After Bonferroni correction, NIVE detected differences in CAT and CAS/CAT biomarkers in elevated BMI children, whereas IMT failed to show a difference. NIVE is a promising technique to monitor radiological biomarkers of subclinical atherosclerosis in the pediatric population.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".