Determinants of discrepancies between two-dimensional echocardiographic methods for assessment of maximal left atrial volume
Bibliographic record
Abstract
AIMS: The determinants of discrepancies among two-dimensional echocardiographic (2D-E) methods for left atrial volume (LAV) assessment are poorly investigated. METHODS AND RESULTS: Maximal LAV was measured in 613 individuals (282 healthy subjects,180 athletes, and 151 hypertensives; age 45 ± 20 years, 62% male) using the ellipsoid model (LAVEllips), the area-length method (LAVAL), and the Simpson's rule (LAVSimps). On the basis of a mathematical model, two left atrial (LA) geometry indexes were tested as predictors of discrepancies between methods: the ratio between LA medial-lateral diameter (MLD) and LA anteroposterior diameter (APD); and the ratio between LA area in the four-chamber view and that of an ellipse with the same diameters [deviation from ellipse (DE)-coefficient]. Discrepancies among methods were consistently present in the overall population and across all study groups. MLD/APD and the DE-coefficient together predicted 76 and 68% of differences between biplane LAVAL and LAVEllips, and between biplane LAVSimps and LAVEllips, respectively. The DE-coefficient was the only determinant of LAVAL/LAVSimps difference (β = 0.167, P < 0.0001). Body mass index was the strongest predictor of discrepancies between single-plane and biplane approaches of LAVAL (β = 0.427, P < 0.0001) and LAVSimps (β = 0.424, P < 0.0001). In additional analyses, biplane LAVAL showed the best agreement with LAV obtained by three-dimensional echocardiography and the best reproducibility and repeatability. CONCLUSION: LA geometry is the main determinant of inconsistencies between 2D-E methods for measuring maximal LAV. Body mass index is the strongest determinant of differences between single-plane and biplane approaches. Different 2D-E methods cannot be used interchangeably for diagnosis and follow-up. The biplane area-length method should be preferred, particularly in overweight-obese subjects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".