Rationale for aortic annuloplasty to standardise aortic valve repair
Bibliographic record
Abstract
Available evidence shows that aortic valve repair reduces valve-related mortality and improves quality of life compared to prosthetic aortic valve replacement. One of the most important predictors of bicuspid and tricuspid aortic valve repair failure is the absence of treating a dilated aortic annulus greater than 25-28 mm. Competency of the aortic valve depends on multiple factors including the diameter of the annulus, sinotubular junction, valve cusps and commissures. Dystrophic aortic insufficiency (AI) is the commonest cause of AI in the Western world and is characterised by dilatation of the aortic annulus (≥25 mm), sinuses and/or sinotubular junction (≥30 mm). Depending on whether the sinuses of Valsalva and/or tubular ascending aorta are dilated, three phenotypes can be identified: dilated aortic root, dilated ascending aorta and isolated AI. All three phenotypes are associated with a dilated aortic annulus. Aortic annuloplasty reduces the dilated aortic annulus and improves the surface of coaptation, as in the case of mitral valve repair. In treating AI, it is also important to restore the physiological sinotubular junction/annulus ratio, which can be carried out with remodeling root repair + subvalvular annuloplasty (for dilated aortic root), tubular ascending aorta replacement + subvalvular annuloplasty (for dilated ascending aorta) and double sub- and supra-valvular annuloplasty (for isolated AI). Aortic annuloplasty is now considered an essential component of aortic valve repair and valve-sparing root surgery.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".