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 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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".