Biomechanics of Aortic Dissection: A Comparison of Aortas Associated With Bicuspid and Tricuspid Aortic Valves
Bibliographic record
Abstract
Background Current methods for aortic dissection risk assessment are inadequate for patients with ascending aortic aneurysms associated with either bicuspid aortic valves (BAVs) or tricuspid aortic valves (TAVs). Biomechanical testing of aortic tissue may provide novel insights and biomarkers. Methods and Results From March 2017 to August 2019, aneurysmal ascending aortas (BAV=23, TAV=23) were collected from elective aortic surgery, normal aortas from transplant donors (n=9), and dissected aortas from surgery for aortic dissection (n=7). These aortas underwent delamination testing in simulation of aortic dissection. Biaxial tensile testing was performed to determine modulus of elasticity (aortic stiffness), and energy loss (a measure of efficiency in performing the Windkessel function). Delamination strength ( S d ) was lowest in dissected aortas (18±6 mN/mm) and highest in normal aortas (58±16 mN/mm), and aneurysms fell in between, with greater S d in the BAV group (37±10 mN/mm) than the TAV group (27±10 mN/mm) ( P <0.001). Bicuspid aortopathy was associated with greater stiffness ( P <0.001), while aneurysms with TAV demonstrated greater energy loss ( P <0.001). S d decreased by 7.8±1.2 mmol/L per mm per decade of life ( r 2 =0.45, P <0.001), and it was significantly lower for patients with hypertension ( P =0.001). S d decreased by 6.1±2.1 mmol/L per mm with each centimeter increase in aortic diameter ( r 2 =0.15, P =0.007). Increased energy loss was associated with decreased S d ( r 2 =0.41), whereas there was no relationship between S d and aortic stiffness. Conclusions Aneurysms with BAV had higher S d than those with TAV, suggesting that BAV was protective. Energy loss was lower in aneurysms with BAV, and inversely associated with S d , representing a potential novel biomarker.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".