Aortic root geometry in bicuspid aortic insufficiency versus stenosis: implications for valve repair†
Bibliographic record
Abstract
OBJECTIVES: The contribution of aortic annular and root disease in bicuspid aortic valve (BAV) insufficiency remains unclear. We compared aortic root geometry between BAV stenosis and aortic insufficiency (AI), before and after repair. METHODS: Patients presenting for surgery for BAV insufficiency (n = 58) were compared with patients with BAV stenosis (n = 58). Clinical and transoesophageal echocardiographic data were collected, including end-diastolic diameters of the ventriculo-aortic junction (VAJ), aortic root, sinotubular junction (STJ) and ascending aorta (AA). RESULTS: AI patients were younger and more likely to be male compared with aortic stenosis (AS) patients. VAJ, aortic root and STJ diameters were significantly larger in AI compared with AS patients (30 ± 0.5 vs 25 ± 0.4 mm, P < 0.001; 41 ± 0.8 vs 34 ± 0.6 mm, P < 0.001; 36 ± 0.9 vs 30 ± 0.6 mm, P < 0.001, respectively). Following multivariable adjustment for age, sex, body surface area and ascending aortic diameter, these diameters remained larger in AI patients with a mean difference of 3, 6 and 4 mm, respectively (all P < 0.001). Mean AA diameter in the AI group was similar to the AS group (37 ± 1.0 vs 34 ± 0.8 mm, P = 0.06). Forty (69%) AI patients had BAV repair with a mean reduction in VAJ and STJ diameters of 5 and 9 mm compared with prerepair (P < 0.0001). CONCLUSIONS: Despite the absence of aortic aneurysms, aortic annulus and root dimensions are significantly larger in patients with BAV insufficiency compared with stenosis. Alterations in aortic root geometry contribute to the pathophysiology of BAV insufficiency and require correction for a successful repair.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".