Mixed valvular heart disease: diagnosis and management
Bibliographic record
Abstract
Mixed valvular diseases (MVDs) are common but have received little attention in the literature, especially regarding the mitral valve (MV) and the right-sided cardiac valves. Whereas echocardiography plays a pivotal diagnostic role, the diagnosis is made difficult due to haemodynamic interactions that may invalidate common indices of severity used in isolated stenosis or regurgitation. The diagnostic strategy should aim at initially separately assessing stenosis and regurgitation, taking into account the diagnostic pitfalls, with complementary use of multimodality imaging in cases of persisting diagnostic uncertainties. Unlike aortic stenosis, the calcium score cannot be used as a surrogate for haemodynamic severity of mixed MV disease. Severe stenosis and/or severe regurgitation are indicative of severe MVD, and management should follow recommendations on the predominant lesion. However, some patients with the combination of moderate stenosis and moderate regurgitation have a poor prognosis when left untreated. Concordant data suggest that, in patients with mixed aortic or MV disease, transvalvular velocities and pressure gradients are more powerful prognostic indicators than valve area or the severity of regurgitation. It is essential to consider the global repercussions that indicate poor outcomes in patients with MVD. However, whereas symptoms and/or ventricular dysfunction are considered as clear indication for intervention, imaging cut-offs have not been validated for balanced moderate regurgitation and stenosis. Although emerging evidence tends to support earlier management, further prospective studies are required, and pending the results of these studies, asymptomatic patients with MVD should be closely monitored.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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 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".