Ascending aortic geometry and its relationship to the biomechanical properties of aortic tissue
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Bibliographic record
Abstract
ObjectiveThe objective of this study was to evaluate the relationship between ascending aortic geometry and biomechanical properties.MethodsPreoperative computed tomography scans from ascending aortic aneurysm patients were analyzed using a center line technique (n = 68). Aortic length was measured from annulus to innominate artery, and maximal diameter from this segment was recorded. Biaxial tensile testing of excised tissue was performed to derive biomechanical parameters energy loss (efficiency in performing the Windkessel function) and modulus of elasticity (stiffness). Delamination testing (simulation of dissection) was performed to derive delamination strength (strength between tissue layers).ResultsAortic diameter weakly correlated with energy loss (r2 = 0.10; P < .01), but not with modulus of elasticity (P = .13) or delamination strength (P = .36). Aortic length was not associated with energy loss (P = .87), modulus of elasticity (P = .13) or delamination strength (P = .90). Using current diameter guidelines, aortas >55 mm (n = 33) demonstrated higher energy loss than those <55 mm (n = 35; P = .05), but no difference in modulus of elasticity (P = .25) or delamination strength (P = .89). A length cutoff of 110 mm was proposed as an indication for repair. Aortas >110 mm (n = 37) did not exhibit a difference in energy loss (P = .40), modulus of elasticity (P = .69), or delamination strength (P = .68) compared with aortas <110 mm (n = 31). Aortas above diameter and length thresholds (n = 21) showed no difference in energy loss (P = .35), modulus of elasticity (P = .55), or delamination strength (P = .61) compared with smaller aortas (n = 47).ConclusionsAortic geometry poorly reflects the mechanical properties of aortic tissue. Weak association between energy loss and diameter supports intervention at larger diameters. Further research into markers that better capture aortic biomechanics is needed.
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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 | 0.000 | 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 it