Clinical application of echocardiographic-derived myocardial strain imaging in subclinical disease
Bibliographic record
Abstract
PROPOSE OF REVIEW: To highlight the evidence to support the measurement of left ventricular (LV) global longitudinal strain (GLS) in the risk assessment and management of patients with valvular heart disease. RECENT FINDINGS: Subclinical myocardial dysfunction that is characterized by impaired LV GLS is often present in patients with valvular disease. The addition of GLS to the LV systolic function assessment refines disease classification and improves both prognosis and management of valvular disease. SUMMARY: The measurement of global systolic function is essential in risk assessment and management of all patients with valvular heart disease. Although LV ejection fraction remains the main parameter of systolic function, strain measurement has emerged as a promising systolic function marker. Strain describes deformation of the myocardium that occurs during the cardiac cycle in the longitudinal, circumferential, and radial planes. Of all the regional strain deformation measurements, evidence gathered over the last decade has shown that GLS improves detection of systolic dysfunction beyond LV ejection fraction and provides additional prognostic information.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".