Controversies in quantification of mitral valve regurgitation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Mitral regurgitation remains a common problem, and when severe, is associated with significant morbidity and mortality. At present, echocardiography remains the primary modality for assessing both mechanism and severity of mitral regurgitation. However, recent studies demonstrate that the echocardiographic assessment of mitral regurgitation severity may be subject to variability as a result of semiquantitative parameters, dependence upon loading conditions and significant interobserver variability. RECENT FINDINGS: Cardiac magnetic resonance (CMR) imaging is the gold standard in the assessment of cardiac function and structure, and offers an alternative method to estimate mitral regurgitation severity. Herein, we discuss the pitfalls of echocardiography in the assessment of mitral regurgitation and describe recent data demonstrating improved accuracy of CMR in the assessment of mitral regurgitation severity. Further, CMR derived regurgitant volume of ≤55 ml is associated with freedom from surgical intervention, in contrast to traditional volumetric measures, which fail to predict the need for surgical intervention. SUMMARY: The CMR assessment of mitral regurgitation severity is easily performed and appears to be more accurate and predictive of the need for surgery than traditional echocardiography. These promising findings require further confirmation in larger outcome trials.
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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) | 0.002 | 0.004 |
| 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".