Impact of contrast echocardiography on accurate discrimination of specific degree of left ventricular systolic dysfunction and comparison with cardiac magnetic resonance imaging
Bibliographic record
Abstract
AIM: Limited data exist on the impact of contrast-enhanced echocardiography on treatment decisions in heart failure patients that require specific left ventricular ejection fraction (LVEF) criteria. This study assessed accuracy of contrast-enhanced echocardiography in identifying patients with LVEF >35% vs ≤35% with cardiac magnetic resonance (CMR) used as reference method. METHODS AND RESULTS: Fifty-five patients from prospective Alberta HEART cohort with LVEF ≤50% on CMR were included. All patients had echocardiography performed within 2 weeks of CMR. Contrast agent was used when ≥2 contiguous LV endocardial segments were poorly visualized on echocardiography. LVEF was computed by Simpson's biplane method using non-contrast echocardiography and contrast-enhanced echocardiography and by outlining the endocardial contours in short-axis cine CMR images. Strong agreement in LV volumes and LVEF was seen between CMR and echocardiography with and without contrast (intra-class correlation coefficients >0.8) with less underestimation of LV volumes by contrast-enhanced echocardiography. Good agreement in LVEF ≤35% vs >35% was seen between CMR and non-contrast echocardiography with optimal images (κ 0.862) and contrast echocardiography (κ 0.769) while it was moderate for non-contrast echocardiography with suboptimal images (κ 0.491). The use of LV contrast in patients with suboptimal images (n = 39) resulted in correctly upgrading LVEF from ≤35% to >35% in 5 (13%) patients and downgrading LVEF from >35% to ≤35% in 2 (5%) patients using CMR as reference. CONCLUSIONS: Contrast-enhanced echocardiography in heart failure patients with suboptimal images helps to more accurately assess eligibility for specific therapies and avoid need for further testing, therefore should be considered routine part of echocardiographic assessment.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".