Echocardiographic evaluation of left ventricular function in patients with mitral regurgitation: a meta-analysis
Bibliographic record
Abstract
Purpose This study aimed to quantitatively assess left ventricular function in patients with mitral regurgitation (MR) using speckle-tracking echocardiography and further clarify its clinical application. Methods PubMed, Embase, Web of Science, and Cochrane Library databases were searched from the date of establishment to November 22, 2024. Two investigators independently screened the literature, extracted data, and evaluated the quality of the included studies. Meta-analysis was performed using Stata18.0 software. Results Finally, 23 studies were included, all of which scored ≥7 on the Newcastle–Ottawa scale. Meta-analysis results showed that, the left ventricular end-systolic volume index ( P < 0.01), left ventricular end-diastolic volume index ( P < 0.01), and left ventricular mass index ( P < 0.01) of patients with MR were higher than those of healthy individuals. In contrast, the left ventricular ejection fraction ( P < 0.01) and global radial strain (GRS) ( P < 0.01) of patients with MR was lower than that of healthy individuals; the difference was significant. The E / A of both groups ( P = 0.22), global circumferential strain ( P = 0.43), global longitudinal strain ( P = 0.10), left ventricular relative wall thickness ( P = 0.20), and some indexes with significant heterogeneity were analyzed by subgroup according to the degree of MR. The combined results of most subgroup analysis were consistent with the overall results and the heterogeneity was reduced. Conclusion There is significant difference in the GRS between patients with MR and healthy individuals, which can provide reference for evaluating left ventricular function in patients with MR.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".