Mitral leaflet separation to evaluate the severity of mitral stenosis: Validation of the index by transesophageal three‐dimensional echocardiography
Why this work is in the frame
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Bibliographic record
Abstract
Background Determining severity of mitral stenosis ( MS ) by planimetry of mitral valve orifice area ( MVA ) has been a challenging issue in clinical practice, especially for less experienced cardiologists. Mitral leaflet separation ( MLS ) has shown a good correlation with MVA measurements. However, it has never been validated against multiplane 3 DTEE planimetry ( MVA 3D ). We aimed to evaluate the accuracy of MLS index ( MLSI 2D ) in predicting MS severity. Methods We prospectively enrolled 144 patients with MS who underwent clinically indicated 2 DTTE and 3 DTEE . MLSI 2D was yield by averaging the maximal leaflet tip distance in diastole, in parasternal long‐axis and apical four‐chamber views. MVA 3D was used as the reference method. Results MLSI 2D showed an excellent discriminatory ability between different grades of MS ( P < .001). There was a significant positive correlation between MLSI 2D and MVA 3D ( r = .93, P < .001) irrespective of concurrent mitral regurgitation ( r = .94, P < .001) and/or atrial fibrillation ( r = .92, P < .001). By receiver operating characteristic ( ROC ) curves, MLSI 2D ≤ 8.6 mm showed 100% sensitivity and 76% specificity for very severe MS . MLSI 2D ≥ 11.2 mm determined progressive MS with 100% sensitivity and 82% specificity. The study population was then divided into a derivation group and a validation group. A regression equation for MVA by MLSI 2D was derived in first group. Then, the MVA was calculated by this equation in validation group and was not significantly different from MVA 3D . Conclusion MLSI 2D showed an excellent ability to assess MS severity and correlates well with planimetered MVA measured by 3 DTEE .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it