Three-dimensional echocardiography: assessment of inter- and intra-operator variability and accuracy in the measurement of left ventricular cavity volume and myocardial mass
Bibliographic record
Abstract
Accurate left ventricular (LV) volume and mass estimation is a strong predictor of cardiovascular morbidity and mortality. We propose that our technique of 3D echocardiography provides an accurate quantification of LV volume and mass by the reconstruction of 2D images into 3D volumes, thus avoiding the need for geometric assumptions. We compared the accuracy and variability in LV volume and mass measurement using 3D echocardiography with 2D echocardiography, using in vitro studies. Six operators measured the LV volume and mass of seven porcine hearts, using both 3D and 2D techniques. Regression analysis was used to test the accuracy of results and an ANOVA test was used to compute variability in measurement. LV volume measurement accuracy was 9.8% (3D) and 18.4% (2D); LV mass measurement accuracy was 5% (3D) and 9.2% (2D). Variability in LV volume quantification with 3D echocardiography was %SEMinter = 13.5%, %SEMintra = 11.4%, and for 2D echocardiography was %SEMinter = 21.5%, %SEMintra = 19.1%. We derived an equation to predict uncertainty in measurement of LV volume and mass using 3D echocardiography, the results of which agreed with our experimental results to within 13%. 3D echocardiography provided twice the accuracy for LV volume and mass measurement and half the variability for LV volume measurement as compared with 2D echocardiography.
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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 | 0.019 | 0.047 |
| 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 | 0.000 | 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".