Echocardiographic assessment of right ventricular volumes: a comparison of different techniques in children after surgical repair of tetralogy of Fallot
Bibliographic record
Abstract
AIMS: Different echocardiographic techniques are available for assessing right ventricular (RV) volumes but their clinical validity has not been well established. We compared the feasibility, reproducibility and accuracy of three different echocardiographic techniques for measuring RV volumes and ejection fraction (EF) in children after tetralogy of Fallot (TOF) repair. METHODS AND RESULTS: Seventy patients (age 14.2 ± 7.3 years) were studied using three-dimensional (3D) volume acquisition analysis (Tomtec, Germany), 2D echo with knowledge-based 3D reconstruction (3DR) (Ventripoint, USA) and the four-chamber area (4C area) methods. Parameters analysed were RV end-diastolic volume (EDV), end-systolic volume and EF. Magnetic resonance imaging (MRI) data were available in 41 patients. Intra- and inter-observer as well as inter-technique variability was assessed using Pearson's correlation analysis (R), coefficient of variance, and Bland-Altman analysis. Feasibility was good for all echo techniques (91% for the 3D, 98% for the 3DR, and 100% for the 4C area method). Intra- and inter-observer variability was low for both 3DR and the 3D echo, while more variability was observed for the 4C method. Compared with MRI volumes, 3DR and 3D underestimated EDV by 6.6 ± 10 and 18.2 ± 17.8 mL, respectively, (P < 0.001), while the 4C area method overestimated the EDV by 9.6 ± 33 mL, not significant due to a wide range. CONCLUSION: Current echocardiographic techniques to assess RV volumes are highly feasible and reproducible in paediatric post-operative TOF patients. When compared with MRI measurements, 3DR was the most accurate technique but requires extra equipment that is not readily available.
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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 | 0.007 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".