Speckle tracking echocardiography in primary mitral regurgitation: should we reconsider the time for intervention?
Bibliographic record
Abstract
Thanks to the improvement in mitral regurgitation (MR) diagnostic and therapeutic management, with the introduction of minimally invasive techniques which have considerably reduced the individual surgical risk, the optimization of the timing for MR "open" or percutaneous surgical treatment has become a main concern which has highly raised scientific interest. In fact, the current indications for intervention in MR, especially in asymptomatic patients, rely on echocardiographic criteria with high severity cut-offs that are fulfilled only when not only mitral valve apparatus but also the cardiac chambers' structure and function are severely impaired, which results in poor benefits for post-operative clinical outcome. This led to the need of new indices to redefine the optimal surgical timing in these patients. Speckle tracking echocardiography provides early markers of cardiac dysfunction due to subtle myocardial impairment; therefore, it could offer pivotal information in this setting. In fact, left ventricular and left atrial strains have already shown evidence about their usefulness in recognizing MR impact not only on symptoms and quality of life but also on cardiovascular events and new-onset atrial fibrillation in these patients. Moreover, right ventricular strain could be used to identify those patients with advanced cardiac damage and different grades of right ventricular dysfunction, which entails higher risks for cardiac surgery that could overweigh surgical benefits. This review aims to describe the importance of reconsidering the timing of intervention in MR and to analyze the potential additive value of speckle tracking echocardiography in this clinical setting.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".