Role of speckle tracking echocardiography to predict LV dysfunction post mitral valve replacement surgery for severe mitral regurgitation
Bibliographic record
Abstract
Background: Despite improvement in the surgical procedure and strictly following the guidelines for mitral valve replacement (MVR), left ventricular dysfunction still occurs. Novel echocardiographic indices can predict development of LV (left ventricle) dysfunction post MVR. LV-GLS (global longitudinal strain) derived from speckle tracking echocardiography, has been proposed as a novel measure to better depict latent LV dysfunction. Methods: A total of 100 patients with severe MR (mitral regurgitation) planned for MVR were included. Speckle tracking echocardiography was performed at baseline and at follow up post MVR. ROC (Receiver operating characteristics) curve was plotted to derive the cutoff value of LV-GLS for prediction of LV dysfunction post MVR. Univariate and multi variate regression was analyzed to predict the independent predictors of LV dysfunction after MVR. Results: LV-GLS was decreased from baseline data (-19.9 vs. -17.7) in patients with LVEF <50% after MVR compared to patients with LVEF≥ 50%. Baseline value of LVESD (35.36 mm vs. 28.23 mm) and LVEDD (49.33 mm vs. 45.10 mm) were significantly higher in patients with LVEF<50% compared to LVEF ≥50% at 3 months follow up. A cutoff value of GLS -19% with sensitivity of 80.3% and specificity of 75.7% was associated in patients with LV dysfunction after MVR. In multivariate regression model GLS < -19% (OR = 21.8, CI:6.61-82.4, P=<0.001) was an independent predictor of LV dysfunction post MVR. Conclusion: A GLS value of less than -19% was demonstrated as an independent predictor of short term LV dysfunction after mitral valve surgery, LVESD ≥40 mm was also verified additional parameter to predict the LV dysfunction post MVR.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".