Aortic root changes before and after surgery for chronic aortic dilatation: A 3D echocardiographic study
Bibliographic record
Abstract
BACKGROUND: Quantitative 3D assessment of the aortic root may improve planning and success of aortic valve (AV)-sparing operations. AIMS: To use 3D transesophageal echocardiography (TEE) to assess the effect of chronic aortic dilatation on aortic root shape and aortic regurgitation (AR) severity and to examine the effects of AV-sparing operations. METHODS AND RESULTS: To determine the changes with chronic aortic dilatation, we studied 48 patients, 23 with aortic dilatation (Group 1 ≤ mild AR, n = 13; Group 2 ≥ moderate AR, n = 10) and 25 Controls. To determine the changes in AV-sparing operations, a subgroup of 15 patients were examined pre- and post surgery. 3D-TEE images were analyzed using multiplanar reconstruction (QLAB, Philips, Philips Medical Systems, Andover, MA, USA) to obtain aortic root areas, diameters, and lengths. We also calculated a novel parameter called total coaptation surface area (TCoapSA), which sums the contact surface area of all the AV cusps. Compared to Controls, Groups 1 and 2 had significantly larger aortic root areas, inter-commissural distances, and cusp heights. Compared to Group 1 and Controls, Group 2 had significantly smaller TCoapSA when adjusted for aortic annular area (P = 0.001) with shorter coaptation height (P < 0.001). In patients undergoing AV-sparing surgery, TCoapSA was significantly larger post surgery (P = 0.001) with greater coaptation height (P < 0.001) and smaller inter-commissural distances (P < 0.001). CONCLUSIONS: The aortic valve is a dynamic structure that remodels in response to aortic dilatation. Successful valve-sparing surgery corrects these changes. Quantitative modeling of the aortic valve and root could potentially improve the repair to the individual patients and modify outcomes.
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How this classification was reachedexpand
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) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".