Role of myocardial strain imaging in diagnosing inducible myocardial ischemia with treadmill contrast-enhanced stress echocardiography
Bibliographic record
Abstract
INTRODUCTION: The aim of this study is to analyze the diagnostic value of global longitudinal strain (GLS) in detecting inducible myocardial ischemia in patients with chest pain undergoing treadmill contrast-enhanced stress echocardiography (SE). METHODS: We retrospectively enrolled all patients who underwent invasive coronary angiography after treadmill contrast-enhanced SE. Rest and peak-stress myocardial GLS, segmental LS, and LS of 4-chamber (CH), 2-CH, and 3-CH views were reported. Luminal stenosis of more than 70% or fractional flow reserve (FFR) of < 0.8 was considered significant. RESULTS: In total 33 patients were included in the final analysis, among whom sixteen patients (48.4%) had significant coronary artery stenosis. Averaged GLS, 3-CH, and 4-CH LS were significantly lower in patients with critical coronary artery stenosis compared to those without significant stenosis (-17.1 ± 7.1 vs. -24.2 ± 7.2, p = 0.041), (-18.2 ± 8.9 vs. -24.6 ± 8.2, p = 0.045) and (-14.8 ± 6.2 vs. -22.8 ± 7.8, p = 0.009), respectively. Receiver operating characteristic (ROC) analysis of ischemic and non-ischemic segments demonstrated that a cut-off value of -20% of stress LS had 71% sensitivity and 60% specificity for ruling out inducible myocardial ischemia (Area under the curve was AUC = 0.72, P < 0.0001). CONCLUSION: Myocardial LS measured with treadmill contrast-enhanced stress echocardiography demonstrates potential value in identifying patients with inducible myocardial ischemia.
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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) | 0.000 | 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".