Is there a role for biomarkers in asymptomatic severe chronic primary mitral regurgitation?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Chronic primary mitral regurgitation (MR) is a common heart valve disease with a rising prevalence with the aging populations worldwide. The timing of surgical intervention in patients who have severe MR but remain asymptomatic is often debated. Herein we synthesize the latest American College of Cardiology/American Heart Association (ACC/AHA) and European Society of Cardiology/ European Association for Cardio-Thoracic Surgery (ESC/EACTS) heart valve guidelines in such patients and illustrate how circulating and/or imaging biomarkers can be used to help refine decision making algorithms. RECENT FINDINGS: The approach to decision making and strength of guideline recommendations in patients with asymptomatic stage C1 (left ventricular ejection fraction [LVEF] > 60% and left ventricular end systolic dimension [LVESD] < 40 mm) and stage C2 disease (LVEF ≤ 60% and/or LVESD ≥ 40 mm) are reviewed. While surgical intervention is clearly indicated in patients with stage C2 disease, a multifaceted approach that integrates repairability, expertise, sub-clinical evidence of left ventricular (LV) dysfunction, and patient preferences is required to identify the optimal approach to surveillance vs. surgery. The role of imaging (3D echocardiography, contrast echocardiography, left ventricular global longitudinal strain, and cardiovascular magnetic resonance imaging [CMR]) and circulating (natriuretic peptides) biomarkers in decision making is also reviewed. SUMMARY: The decision making around timing of intervention in chronic primary MR requires a personalized approach that is based on accurate assessments of severity of MR, LV dimensions, LV function, valve morphology/repairability, surgeon and center expertise, and patient wishes. Biomarkers hold promise in refining decision making.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".