Ultrasound vector flow imaging during veno-arterial extracorporeal membrane oxygenation in a thoracic aorta model
Bibliographic record
Abstract
In veno-arterial extracorporeal membrane oxygenation (VA-ECMO) treatment, the mixing zone is a key hemodynamic factor that determines the efficacy of the treatment. This study aimed to evaluate the applicability of a novel ultrasound technique called vector flow imaging (VFI) for visualizing complex flow patterns in an aorta phantom under VA-ECMO settings. VFI experiments were performed to image aortic hemodynamics under VA-ECMO treatment simulated in an anthropomorphic thoracic aorta phantom using a pulsatile pump (cardiac output: 2.7 L/min) and an ECMO pump with two different flow rates, 0.35 L/min and 1.0 L/min. The cardiac cycle of hemodynamics in the ascending aorta, aortic arch, and descending aorta was visualized, and the spatio-temporal dynamics of flow vectors were analyzed. VFI successfully visualized dynamic flow patterns in the aorta phantom. When the flow rate of the ECMO pump increased, ECMO flow was more dominant than cardiac output in the diastole phase, and the speed of cardiac output was suppressed in the systole phase. Vortex flow patterns were also detected in the ascending aorta and the arch under both ECMO flow rate conditions. The VFI technique may provide new insights into aortic hemodynamics and facilitates effective and safe VA-ECMO treatment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".