Abstract 14914: Advanced 4D-flow Measurements of Aortic Forward Flow, Reverse Flow, and Stasis in Bicuspid Aortic Valve Patients Without Aortic Stenosis or Regurgitation
Bibliographic record
Abstract
Introduction: Precise analysis of aortic hemodynamics is crucial in the study of bicuspid aortic valve (BAV) disease. This study provides a comprehensive evaluation of aortic forward flow (FF), reverse flow (RF) and stasis in BAV patients using novel 3D-based techniques previously shown to be more accurate than traditional 2D analysis methods. Hypothesis: BAV patients without valve dysfunction show abnormal aortic FF, RF, and stasis compared to healthy controls. Methods: We recruited 44 BAV patients (48±15 yrs, 27% female) and 23 healthy controls (37±14 yrs, 35% female). Cardiac MRI at 3T was performed inclusive of 4D-flow imaging. Patients with any aortic stenosis (AS) or ≥mild regurgitation (AR) were excluded. Flow analysis was performed by segmented volumetric regions: left ventricular outflow tract (LVOT), ascending aorta (AAo), arch, proximal descending aorta (PDAo), and distal descending aorta (DDAo). In each region, forward flow (FF), reverse flow (RF) and stasis were averaged over the cardiac cycle on a voxel-by-voxel basis. Left ventricular (LV) end-diastolic volume, end-systolic volume and ejection fraction were also measured. T-tests (or non-parametric equivalent) compared differences in parameters between cohorts. Results: BAV patients were significantly older than controls (48±15 vs. 37±14 yrs; p=0.01) but exhibited no significant differences in LV measures. Patients showed reduced FF in the AAo (0.09±0.03 vs. 0.11±0.04 mL/cycle; p<0.01), but greater FF in all downstream regions (eg. PDA: 0.02±0.03 vs. 0.01±0.02 mL/cycle; p=0.01). RF was significantly elevated in patients in the AAo (0.06±0.02 vs. 0.02±0.02 mL/cycle; p<0.01). BAV patients exhibited significantly less stasis in every region except the DDAo (eg. AAo: 23±11 vs. 50±10 % of cardiac cycle). Conclusions: 3D-derived measurements of FF, RF, and stasis are significantly altered in the thoracic aorta of BAV patients in the absence of AS or AR.
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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) | 0.003 | 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".