International Expert Consensus on Sutureless and Rapid Deployment Valves in Aortic Valve Replacement Using Minimally Invasive Approaches
Bibliographic record
Abstract
OBJECTIVE: To define the benefit of sutureless and rapid deployment valves in current minimally invasive approaches in isolated aortic valve replacement. METHODS: A panel of 28 international experts with expertise in both minimally invasive aortic valve replacement and rapid deployment valves was constituted. After thorough literature review, the experts rated evidence-based recommendations in a modified Delphi approach. RESULTS: No guideline could be retrieved. Thirty-three clinical trials and 9 systematic reviews could be identified for detailed text analysis to obtain a total of 24 recommendations. After rating by the experts 12, final recommendations were identified: preoperative computed tomographic scan as well as intraoperative transesophageal echocardiography are highly recommended. Suitable annular sizes are 19 to 27 mm. There is a contraindication for bicuspid valves only for type 0 and for annular abscess or destruction due to infective endocarditis. The use of sutureless and rapid deployment valves reduces extracorporeal circulation and aortic cross-clamp time and leads to less early complications as prolonged ventilation, blood transfusion, atrial fibrillation, pleural effusions, paravalvular leakages and aortic regurgitation, and renal replacement therapy, respectively. These clinical outcomes result in reduced intensive care unit and hospital stay and reduced costs. The use of sutureless and rapid deployment valves will lead to a higher adoption rate of minimally invasive approaches in aortic valve replacement. Respect should be taken to a necessary short learning curve for both sutureless and minimally invasive programs. CONCLUSIONS: Sutureless and rapid deployment aortic valve replacement together with minimally invasive approaches offers an attractive option in aortic valve placement for patients requiring biological valve replacement.
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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 | 0.060 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".