Validation of 3D Echocardiographic Assessment of Left Ventricular Volumes, Mass, and Ejection Fraction in Neonates and Infants With Congenital Heart Disease
Bibliographic record
Abstract
BACKGROUND: quantitative assessment and validation of left ventricular (LV) volumes and mass in neonates and infants with complex congenital heart disease (CHD) is important for clinical management but has not been undertaken. We compared matrix-array 3D echocardiography (3D echo) measurements of volumes, mass, and ejection fraction (EF) with those measured by cardiac MRI in young patients with CHD and small LVs because of either young age or LV hypoplasia. METHODS AND RESULTS: thirty-five patients aged <4 years (median, 0.8 years) undergoing MRI were prospectively enrolled. Three-dimensional echo was acquired immediately after MRI, and volume, mass, and EF measurements, using summation of discs methodology, were compared with MRI. Three-dimensional echo end-diastolic volume (24.4±15.7 versus 24.8±46.4 mL; P=0.01; intraclass correlation coefficient [ICC], 0.96) and end-systolic volume (12.3±8.6 versus 9.6±6.8 mL; P<0.001; ICC, 0.90) correlated with MRI with small mean differences (-0.49 mL [P=0.6] and 2.7 mL [P=0.001], respectively). Three-dimensional echo EF was smaller than MRI by 9.3% (P<0.001), and 3D echo LV mass measurements were comparable to MRI (17.3±10.3 versus 17.6±12 g; P<0.77; ICC, 0.93), with a small mean difference (1.1 g; P=0.28). There was good intra- and interobserver reliability for all measurements. CONCLUSIONS: in neonates and infants with CHD and small LVs (age appropriate or hypoplastic), matrix-array 3D echo measurements of mass and volumes compare well with MRI, providing an important modality for ventricular size and performance analysis in these patients, particularly in those with left-side heart obstructive lesions.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".