Impact of age, sex, and global function on normal aortic hemodynamics
Bibliographic record
Abstract
PURPOSE: To examine the effects of age, sex, and left ventricular global function on velocity, helicity, and 3D wall shear stress (3D-WSS) in the aorta of N = 100 healthy controls. METHODS: Fifty female and 50 male volunteers with no history of cardiovascular disease, with 10 volunteers per age group (18-30, 31-40, 41-50, 51-60, and 61-80 years) underwent aortic 4D-flow MRI. Quantification of systolic aortic peak velocity, helicity, and 3D-WSS distribution and the calculation of age group-averaged peak systolic velocity and 3D-WSS maps ("atlases") were computed. Age-related and sex-related changes in peak velocity, helicity, and 3D-WSS were computed and correlated with standard metrics of left ventricular function derived from short-axis cine MRI. RESULTS: No significant differences were found in peak systolic velocity or 3D-WSS based on sex except for the 18- to 30-year-old group (males 8% higher velocity volume and 3D-WSS surface area). Between successively older groups, systolic velocity decreased (13%, <1%, 7%, and 55% of the aorta volume) and 3D-WSS decreased (21%, 2%, 30%, and 62% of the aorta surface area). Mean velocity, mean 3D-3D-WSS, and median helicity increased with cardiac output (r = 0.27-0.43, all P < .01), and mean velocity and 3D-WSS decreased with increasing diameter (r > 0.35, P < .001). Arch and descending aorta systolic mean velocity, mean 3D-WSS, and median helicity increased with normalized left ventricular volumes: end diastolic volume (r = 0.31-0.37, P < .01), end systolic volume (r = 0.27-0.35, P < .01), and stroke volume (r = 0.28-0.35, P < .01). CONCLUSION: Healthy aortic hemodynamics are dependent on subject age, and correlate with vessel diameter and cardiac function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".