Abstract 2663: How Do Cardiologists and Cardiothoracic Surgeons Treat Asymptomatic Mitral Regurgitation in Clinical Practice? An International Survey
Bibliographic record
Abstract
Background: Severe mitral regurgitation (MR) is known to be associated with adverse clinical outcomes. Thus, consensus-derived, evidence-based practice standards (e.g., ACC/AHA Guidelines for Management of Valvular Heart Disease) have been published. Yet, no data exist to describe whether physicians follow such standards in clinical practice for asymptomatic pts with MR. Methods: A random sample of cardiovascular specialists were surveyed by email and asked to complete 26 items that encompassed MR-related practice patterns. Results: 1035 physicians completed the survey (68% response rate) and the sample included adult cardiologists (95%) and cardiac surgeons (5%) who practice in the USA (84%), Canada (6%), and other nations (10%). When asked ``Do you refer asymptomatic patients with severe MR and normal LV function for MV repair?”, 28% responded yes/almost always, and 11% responded no/ rarely. There was geographic & specialty-dependent variation in practice (Table ). Patient referral for mitral surgery was based on risk markers, such as atrial fibrillation (18%) and pulmonary hypertension (17%) and anatomic factors (e.g., flail valve, 18%) and clinical variables (e.g., increased likelihood of repair, 19%). Most physicians (65%) use medications to delay progression of MR, with ACE-inhibitors utilized by 57%. Isolated posterior prolapse repair was repaired successfully >85% at their hospital by 61% (60% for cardiologists vs. 82% for surgeons, p=0.004). 28% of respondents almost always quantitate MR using effective regurgitant orifice area, while 30% rarely or never do so. Conclusions: Cardiologists frequently refer asymptomatic MR patients for mitral reparative surgery, but referral is often prompted by factors beyond those included in current guidelines. Practice patterns vary by physician type and by geographical location. Medications are frequently used to treat asymptomatic individuals with MR, in the absence of documented evidence of efficacy.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".