Surgical repair of ischemic mitral regurgitation: one ring does not fit all
Bibliographic record
Abstract
PURPOSE OF REVIEW: The review summarizes the key parameters that can aid in determining the optimal treatment of ischemic mitral regurgitation (IMR). RECENT FINDINGS: Left ventricular (LV) and mitral valve (MV) parameters are important for surgical planning and risk stratification in IMR. Although LV dimensions is one of the main parameters used in the guidelines, volumes more accurately depict LV remodelling. Furthermore, wall motion abnormalities and wall motion score index can also be useful for surgical planning in treatment of IMR. Viability is best measured with cardiac magnetic resonance, but it is not feasible in certain centres. In contrast, measurement of strain with echocardiography is an emerging and feasible tool for estimating viability. MV leaflet tethering and pattern measured with echocardiography are also useful for MV surgery. Anterior leaflet excursion angle can identify patients in whom undersized ring annuloplasty is potentially unsuitable. SUMMARY: Treatment of IMR relies on accurate parameters that can determine the optimal surgical approach. In some patients, lack of viable myocardium suggests inadequacy of revascularization and thus, an adjunctive left ventricular reconstruction may be necessary. Degree and pattern of MV leaflet tethering can indicate whether ring annuloplasty, which is the most common repair technique, is sufficient or an adjunctive sub-valvular intervention is beneficial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".