Are there surgical implications to aortic root motion?
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE STUDY: By increasing the longitudinal stress in the ascending aorta, downward movement of the aortic root might promote the proximal transverse tears seen in aortic dissections. The study aim was to evaluate the influence of five common cardiac conditions on the magnitude of aortic root displacement in cardiac patients. METHODS: Aortic root contrast injections were analyzed in 90 patients (mean age 68 years) to measure downward motion of the root perpendicular to the plane of the sinotubular junction (STJ). RESULTS: Displacement of the aortic root ranged from 0 to 14 mm (mean 4.8 mm). Patients with aortic insufficiency (AI) showed increased aortic root movement (7.3 versus 4.3 mm, p = 0.003), whereas those with left ventricular hypokinesis (3.7 versus 5.5 mm, p = 0.014) or with myocardial hypertrophy (3.8 versus 5.1 mm, p = 0.073) exhibited reduced downward movement. These variables were independent, and correlated with the magnitude of aortic root motion. A stress analysis of the aortic root, arch and branches of the arch determined that the longitudinal stress approximately 2 cm above the STJ, in the outer curve of the aorta, was increased by 32% in patients with AI compared to patients without AI. CONCLUSION: Patients with cardiac conditions associated with increased aortic root motion such as AI may be at greater risk of aortic dissection because of increased longitudinal stress in the ascending aorta. Therefore, AI should be used as an indicator and aortic root displacement monitored to prevent the risk of aortic dissection.
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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 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".