Seven-Year Results for RESILIA Tissue in Bicuspid Aortic Valve Replacement Patients: Age and Valve Size Considerations
Bibliographic record
Abstract
OBJECTIVES: Patients with bicuspid aortic valve disease requiring surgical aortic valve replacement are often younger and want to avoid lifelong anticoagulation. A multicentre single-arm non-randomized study, the COMMENCE trial, studied outcomes of RESILIA tissue aortic valves in bicuspid aortic valve patients through 7 years of follow-up. METHODS: Of 672 patients who underwent surgical replacement of native aortic valves, 214 had bicuspid and 458 had tricuspid aortic valves. Propensity score analyses with inverse probability of treatment weighting were utilized to minimize bias due to measured confounders. Linear mixed-effect models compared longitudinal changes in haemodynamic parameters. RESULTS: Patients with bicuspid were significantly younger than those with tricuspid aortic valves-mean age of bicuspid: 59.8 (12.4) vs tricuspid: 70.2 (9.5) years; P < .001; 39/214 (18%) bicuspid aortic valve patients were <50 years old. There was no evidence of structural valve deterioration in any bicuspid aortic valve patients over 7 years of follow-up. At 7 years, there was no significant difference between bicuspid and tricuspid aortic valve patients in propensity score- and age-adjusted survival (91.9% vs 88.1%, respectively; P = .35), stroke, or reoperation. Among bicuspid aortic valve patients <65 years of age, there was no significant difference in prosthetic valve effective orifice areas and mean gradients between 3 months and 7 years postoperatively. CONCLUSIONS: Patients with bicuspid aortic valves had excellent outcomes with RESILIA tissue valves at 7 years with no evidence of structural valve deterioration. These results suggest a durable alternative for carefully selected younger patients wishing to avoid anticoagulation. CLINICAL TRIAL REGISTRATION NUMBER: NCT01757665.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".