Quantitative approach to aortic valve-sparing surgery
Bibliographic record
Abstract
Our goal was to understand why it is difficult to achieve reliable valve competence after aortic valve-sparing surgery, and to propose quantitative data aimed at improving the outcome of the procedure. Valve-sparing procedures were performed in patients with dilated aortic roots and aortic regurgitation, and reproduced in physical models to explore what should be the restored dimensions of the aortic root and leaflets for valve sparing to be successful. In parallel, a three-dimensional geometric model of the aortic valve was tested to evaluate its capability to predict the annulus diameter, sinotubular junction diameter, valve height, and leaflet free-edge length and height in competent spared valves. Valve sparing resulted in more or less severe residual regurgitation in all the patients considered. Successful valve-sparing was achieved in vitro by making further changes to the annulus diameter, the leaflet free-edge length and/or graft size. The changes needed were effectively predicted by the geometric model. Tabulated valve dimensions allowing restoration of competence were generated for convenient use by surgeons. A quantitative approach to aortic valve sparing is proposed, putting emphasis on the functional characteristics of the restored valve geometry.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".