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 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 | 0.002 |
| 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".