A Multicenter Study of Three-dimensional Echocardiographic Evaluation of Normal Pediatric Left Ventricular Volumes and Function with Automated Versus Semi-Automated Quantification
Bibliographic record
Abstract
Background: Three-dimensional echocardiography (3DE) evaluation of left ventricular (LV) volume and function in pediatrics compares favorably with cardiac magnetic resonance imaging. A multicenter trial with automated and semi-automated LV quantification allows for generation of normative data in large pediatric patients. The aims of this study were to evaluate the feasibility and reproducibility of measuring three-dimensional echocardiography (3DE) volumes and function in pediatric patients in a multicenter trial; to determine if automated software (without contouring edits) will improve the reproducibility in volume and function analysis; and thus establish normal z score values in this unique population. Methods: Six hundred and ninety-eight healthy children (ages 0 to 18 years) were recruited from 5 centers. Left ventricular (LV) 3DE was acquired from the 4-chamber view. A vendor independent software analyzed end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), and ejection fraction (EF) using automated and semi-automated quantification. Feasibility and reproducibility were assessed. Body surface area (BSA) based z-scores were generated. Results: Feasibility was 79% (523/658). Reproducibility was good between centers using the semi-automated quantification. Reproducibility was decreased using the automated quantification. Therefore, Z-scores were generated for ESV, EDV, and SV using the semi-automated method. Conclusions: 3DE can reliably evaluate LV volumes and EF in pediatric patients at different centers. We report pediatric Z-scores for normal LV volumes using the semi-automated method. Further optimization of technology will be necessary for reliable use of fully automated quantification by 3DE in children.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".