Mitral leaflet separation to evaluate the severity of mitral stenosis: Validation of the index by transesophageal three‐dimensional echocardiography
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 3DTEE planimetry (MVA3D). We aimed to evaluate the accuracy of MLS index (MLSI2D) in predicting MS severity. Methods We prospectively enrolled 144 patients with MS who underwent clinically indicated 2DTTE and 3DTEE. MLSI2D was yield by averaging the maximal leaflet tip distance in diastole, in parasternal long‐axis and apical four‐chamber views. MVA3D was used as the reference method. Results MLSI2D showed an excellent discriminatory ability between different grades of MS (P < .001). There was a significant positive correlation between MLSI2D and MVA3D (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, MLSI2D ≤ 8.6 mm showed 100% sensitivity and 76% specificity for very severe MS. MLSI2D ≥ 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 MLSI2D was derived in first group. Then, the MVA was calculated by this equation in validation group and was not significantly different from MVA3D. Conclusion MLSI2D showed an excellent ability to assess MS severity and correlates well with planimetered MVA measured by 3DTEE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".