Abstract 18390: Aortic Pulse Wave Velocity Measurement by Doppler Technique During Transesophageal Echocardiography Examination: A Feasible and Reproducible Method
Bibliographic record
Abstract
Introduction: Aortic pulse wave velocity (aPWV) is the gold-standard measure of arterial stiffness. In daily practice, the carotid-femoral PWV (cfPWV) is widely used for arterial stiffness assessment. PWV is calculated by dividing traveled distance by transit time. But, the inaccuracy of distance measurement and the approximation of the true aortic length which change with age may influence the absolute value of cfPWV. Making the assumption that the flow wave of continuous spectral Doppler corresponds to the real pulse wave, aPWV can be directly measured in the aorta by transesophageal echography (TEE). The aim of this study is to evaluate whether aPWV measured by Doppler technique during TEE is easily feasible and to analyse the reproducibility of the technique. Methods: Doppler images were obtained from the TEE examination of the thoracic aorta in 44 patients undergoing elective cardiac surgery. From a random choice of 194 acquisitions, a total of 100 images were analyzed offline by two different investigators in two separated periods to measure the transit time according to the two tangents method. Results: The Bland-Altman plots illustrate that there is no significant difference nor bias between the two readings made by one observer and neither for the mean of the observations made by separated observers. Intra-observer reproducibility of the Doppler measurements was high ( Intra Class Correlation (ICC) = 0.98 and 0.96). Agreement between observer was also good (ICC=0.98). Conclusions: These data demonstrate that Doppler technique during TEE is a feasible and reproducible method to calculate aPWV.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".