Functional Hemodynamic Monitoring With a Wireless Ultrasound Patch
Bibliographic record
Abstract
In this Emerging Technology Review, a novel, wireless, wearable Doppler ultrasound patch is described as a tool for resuscitation. The device is designed, foremost, as a functional hemodynamic monitor-a simple, fast, and consistent method for measuring hemodynamic change with preload variation. More generally, functional hemodynamic monitoring is a paradigm that helps predict stroke volume response to additional intravenous volume. Because Doppler ultrasound of the left ventricular outflow tract noninvasively measures stroke volume in realtime, it increasingly is deployed for this purpose. Nevertheless, Doppler ultrasound in this manner is cumbersome, especially when repeat assessments are needed. Accordingly, peripheral arteries have been studied and various measures from the common carotid artery Doppler signal act as windows to the left ventricle. Yet, handheld Doppler ultrasound of a peripheral artery is susceptible to human measurement error and statistical limitations from inadequate beat sample size. Therefore, a wearable Doppler ultrasound capable of continuous assessment minimizes measurement inconsistencies and smooths inherent physiologic variation by sampling many more cardiac cycles. Reaffirming clinical studies, the ultrasound patch tracks immediate SV change with excellent accuracy in healthy volunteers when cardiac preload is altered by various maneuvers. The wearable ultrasound also follows jugular venous Doppler, which qualitatively trends right atrial pressure. With further clinical research and the application of artificial intelligence, the monitoring modalities with this new technology are manifold.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".