The feasibility of a novel, wearable Doppler ultrasound to track stroke volume change in a healthy adult
Bibliographic record
Abstract
Abstract: This technical note describes the feasibility of measuring and monitoring the common carotid Doppler spectrogram using a novel, wearable ultrasound patch. In addition, we compare this new ultrasound method to various stroke volume (SV) monitors during preload modifying maneuvers. SV changes were analyzed using a stand-squat-stand (SSS) maneuver performed by a volunteer. Hemodynamic changes induced by SSS were captured simultaneously using a pulse-contour analysis SV monitor as well as the novel Doppler ultrasound patch over the common carotid artery; concurrently, the subject measured either: (I) the velocity time integral (VTI) of the descending aorta in one set of SSS maneuvers or (II) bioreactance SV in a second set of SSS maneuvers. On squat, SV consistently rose by pulse contour analysis—by 24.7% and 38.5% in the two protocols, respectively. Concordantly, VTI of the descending aorta and SV by bioreactance also increased—by 39.3% and 38.3%, respectively. Furthermore, both the VTI and corrected flow time (FTc) of the common carotid artery increased with squat during the two protocols (+14.5%; +45.4% and +7.3%; +16.8%, respectively); conversely, all metrics fell from squat-to-stand. In all instances, there was good clinical correlation between the devices. Measuring and trending the common carotid artery Doppler spectrogram using a wearable, hands-free Doppler ultrasound is feasible. There was good clinical correlation with other accepted technologies during preload modifying maneuvers and the change in standard deviation of the VTI from the wearable patch was roughly one-half that of a traditional, hand-held ultrasound probe. Further testing in healthy subjects is warranted.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".