Tips and tricks in addressing mitral annular calcification in mitral valve surgery
Bibliographic record
Abstract
Mitral annular calcification (MAC) is a heterogeneous condition that can complicate mitral valve (MV) repair and replacement operations. Surgery in the context of MAC varies considerably. Relatively simple repairs or replacements can be performed in several patients without the need to remove the calcium bar. However, extensive annular debridement, reconstruction of the atrioventricular junction, reconstruction of the intervalvular fibrous body, and mitral and aortic valve replacement may be necessary in some instances. Outcomes of these patients are directly related to the avoidance of technical problems amid a variety of potential anatomic challenges and pitfalls. Careful preoperative assessment, patient selection, detailed preoperative planning, and intraoperative judgment are required to optimize the chance of a successful outcome. Here we describe our approach to the MV patients with MAC, including preoperative planning, intraoperative technical tips and tricks, as well as a discussion of outcomes and remaining questions in this challenging population. This review only includes patients with MAC and MV dysfunction. It excludes those with associated aortic valve disease. The article contains a lecture on MV surgery in patients with MAC. We also highlight emerging experimental approaches, including hybrid and percutaneous techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".