Predictors of Aortic Valve Repair Failure
Bibliographic record
Abstract
Aortic valve repair is the preferred approach for the treatment of severe aortic insufficiency (AI), as it allows patients to keep their native aortic valve, thus substantially reducing the risk of prosthesis-related complications. Several studies have documented excellent long-term outcomes of aortic valve repair. The major complication of this operation is AI recurrence, with ensuingneed for reoperation. The surgical experience accumulated over the last two decades has allowed for better understanding of the mechanisms of recurrent AI after aortic valve repair. Herein, we review the current state of knowledge on predictors of aortic valve repair failure. These include unaddressed annular dilation, residual cusp prolapse or retraction, commissural orientation, and use of patch material. This enhanced understanding has led to the development of increasingly refined techniques and improved patient outcomes. Continued follow-up and detailed data collection at the time of surgery, together with three-dimensional echo imaging, will allow further improvements in aortic valve 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".