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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".