Myocardial Contrast Echocardiography in the Evaluation of Hypertensive Heart Disease
Bibliographic record
Abstract
Myocardial contrast echocardiography (MCE) has an established role in left ventricular assessment by improving the ventricular opacification and endocardial border definition especially in patients with sub-optimal echocardiographic images. With advances in cardiac ultrasound imaging technology and the development of new contrast agents, the clinical utility of this technique has greatly expanded to include assessment of coronary reperfusion in the setting of acute myocardial infarction, determination of myocardial viability within infarct zones as well as assessment of coronary microcirculation and flow reserve in patients with microvascular coronary disease. Improvements in image quality with intravenous contrast agents can facilitate image acquisition and enhance delineation of regional wall motion abnormalities at peak levels of exercise. Numerous studies have confirmed the clinical utility of contrast enhancement during echocardiographic studies, particularly in patients undergoing stress testing. In this paper, we explore the evidence in support of MCE and its potential clinical applications. Our review aims to summarize (1) the basic principles of myocardial contrast echocardiography including recent advances in the ultrasound technology and contrast agents (2) its clinical applications in the diagnosis of cardiovascular diseases and finally, (3) its potential role in risk stratification and assessment of microvascular perfusion in patients with hypertensive heart disease. Cardiol Res. 2011;2(6):259-268 doi: https://doi.org/10.4021/cr93w
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".