Mitral Annular Systolic Velocities Predict Left Ventricular Wall Motion Abnormality During Dobutamine Stress Echocardiography
Bibliographic record
Abstract
BACKGROUND: Longitudinal systolic left ventricular contraction is complementary to the radial performance and can be assessed using tissue Doppler imaging (TDI). This study was performed to evaluate the contribution of mitral annular systolic velocities using TDI after dobutamine stress echocardiography (DSE). METHODS AND RESULTS: Fifty subjects with suspected coronary artery disease and chest pain were examined, using DSE as usual, as well as TDI imaging of the mitral annulus at the septal, lateral, inferior, anterior, posterior regions and the proximal anteroseptal region from the apical views, before and immediately after DSE. In 24 subjects the study was normal, while wall motion abnormality was seen in 26, 9 of them only after DSE. Mitral annular systolic velocity at the 6 locations increased significantly after DSE both in normal subjects and in those with wall motion abnormality (WMA). After DSE mitral annular septal systolic velocity in normals, 19.2 ± 3.8 cm/sec, was higher than in those with WMA, 14.6 ± 2.5 cm/sec, P < 0.0003. Post-DSE mitral systolic velocity was senstive and accurate in predicting WMA. CONCLUSIONS: Systolic mitral TDI velocities increase after DSE, however to a lesser extent in those with wall motion abnormality, and can differentiate them from normal subjects.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".