Imaging in Minimally Invasive Mitral Valve Repair
Bibliographic record
Abstract
Minimally invasive mitral valve (MV) repair and replacement techniques have evolved as increasingly used alternatives to open heart surgery especially in patients unsuitable for surgery or at high perioperative risk. The vast majority of these techniques are still under development, and only a limited number of devices are approved for clinical use; however, they do show promising results in terms of minimizing complications and improving clinical outcome. The complex anatomy of the MV and the annulus complicates not only the development of devices for minimally invasive MV repair or replacement but also complicates preprocedural planning, which is mandatory for minimization of postprocedural complications. Although echocardiography still plays a crucial clinical role, cross-sectional imaging modalities such as coronary computed tomography (CCT) and cardiac magnetic resonance imaging (CMR) are increasingly gaining importance for preprocedural planning. Specifically, CCT seems to be more practicable for preinterventional planning and device sizing, whereas CMR has been shown useful in detecting increased risk for adverse clinical outcomes through delayed gadolinium enhancement and the monitoring of cardiac morphology and function in the framework of follow-up examinations. This article describes recent techniques currently available or under development for minimally invasive repair or replacement of the MV and reviews the current state of CCT and CMR imaging for preinterventional and postinterventional diagnostic workup.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".