Aortic valve repair with ascending aortic aneurysms: associated lesions and adjunctive techniques
Bibliographic record
Abstract
OBJECTIVE: Patients with supracoronary ascending aortic aneurysms can have aortic insufficiency (AI) due to dilatation of the sinotubular junction and/or associated cusp pathology. The incidence and types of cusp lesions as well as the effect of AI severity and cusp repair techniques on outcome in this patient population is not well defined. METHODS: Since 1996, 55 patients (mean age: 65 ± 13 years, 17 bicuspid valves) presented with supracoronary ascending aortic aneurysms and AI that was mild/moderate in 27 (49%) and severe in 28 (51%). Associated pathology included cusp prolapse in 18 (33%), cusp restriction in nine (16%) and both in three (5%). All patients underwent aortic replacement and remodeling of the sinotubular junction. Adjunctive techniques included subcommissural annuloplasty in 38(69%) and cusp repair in 28 (51%). RESULTS: AI severity was not significantly associated with the presence of cusp pathology (p=0.35). Cusp disease was present in 100% of bicuspid aortic valves compared with only 34% of trileaflet valves (p<0.001). There was no hospital mortality and overall survival was 94 ± 4% and 75 ± 10%, respectively, at 5 and 7 years. Freedom from re-operation was 100% at 7 years and freedom from recurrent AI (>2+) was 87 ± 7% at 5 years. Neither the presence of preoperative severe AI, nor the need for cusp repair was predictive of late outcome. CONCLUSIONS: Cusp pathology is frequently encountered in patients with ascending aortic dilatation and AI. Severe AI is not a contraindication to valve-preserving surgery, but careful identification and repair of cusp pathology, in addition to sinotubular junction reduction, is critical for durable, long-term outcome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".