Comparison of left atrial strain measured by feature tracking computed tomography and speckle tracking echocardiography in patients with aortic stenosis
Bibliographic record
Abstract
AIMS: Peak left atrial longitudinal strain (PALS) is a marker of the left atrial (LA) reservoir function. Novel feature tracking (FT) software allows assessment of LA strain from multidetector computed tomography (MDCT) data. This study aimed at evaluating the agreement between speckle tracking echocardiography (STE) and FT MDCT for the measurement of PALS in patients with sinus rhythm (SR) and with atrial fibrillation (AF). METHODS AND RESULTS: The current study included 318 patients (80 ± 7 years, 54% male) with dynamic MDCT data acquired prior to transcatheter aortic valve implantation. PALS was measured by transthoracic echocardiography using STE (PALSecho) and MDCT using dedicated FT software (PALSCT). In the overall population, the median values of PALSecho and PALSCT were 19.0 [interquartile range (IQR) 12.0-25.0] % and 15.3 (IQR 9.2-19.7) %, respectively. High correlation between PALSecho and PALSCT was observed (r = 0.789, P < 0.001) with a mean bias of -3.7%. The correlation between PALSecho and PALSCT was better among patients with SR (N = 258; r = 0.704, P < 0.001) as compared to patients with AF (N = 60; r = 0.622, P < 0.001). CONCLUSION: PALSecho and PALSCT showed a good agreement in patients with severe aortic stenosis (AS) regardless of the cardiac rhythm. FT MDCT may be an important adjuvant modality for assessing LA reservoir function in patients with severe AS.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".