Distribution of blood flow velocity in the normal aorta: Effect of age and gender
Bibliographic record
Abstract
PURPOSE: To apply flow distribution analysis in the entire aorta across a wide age range from pediatric to adult subjects. MATERIAL AND METHODS: In all, 98 healthy subjects (age 9-78 years, 41 women) underwent 4D flow MRI at 1.5T and 3T for the assessment of 3D blood flow in the thoracic aorta. Subjects were categorized into age groups: group 1 (n = 9, 5 women): 9-15 years; group 2 (n = 13, 8 women): 16-20 years; group 3 (n = 27, 14 women): 21-39 years; group 4 (n = 40, 11 women): 40-59 years; group 5 (n = 9, 3 women): >60 years. Data analysis included the 3D segmentation of the aorta, aortic valve peak velocity, mid-ascending aortic diameter, and calculation of flow velocity distribution descriptors (mean, median, standard deviation, incidence of velocities >1 m/s, skewness, and kurtosis of aortic velocity magnitude). Ascending aortic diameter was normalized by body surface area. RESULTS: Age was significantly associated with normalized aortic diameter (R = 0.73, P < 0.001), skewness (R = 0.76, P < 0.001), and kurtosis (R = 0.74, P < 0.001), all adjusted by heart rate. Aortic peak velocity and velocity distribution descriptors, adjusted by heart rate, were significantly different between age groups (P < 0.001, analysis of covariance). Skewness and kurtosis significantly increased (P < 0.001) during adulthood (>40 years) as compared with childhood (<21 years). Men and women revealed significant differences (P ≤ 0.05) for peak velocity, incidence, mean, median, standard deviation, and skewness, all adjusted by heart rate. CONCLUSION: Aortic hemodynamics significantly change with age and gender, indicating the importance of age- and gender-matched control cohorts for the assessment of the impact of cardiovascular disease on aortic blood flow. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;47:487-498.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".