A novel, hands-free ultrasound patch for continuous monitoring of quantitative Doppler in the carotid artery
Bibliographic record
Abstract
Quantitative Doppler ultrasound of the carotid artery has been proposed as an instantaneous surrogate for monitoring rapid changes in left ventricular output. Tracking immediate changes in the arterial Doppler spectrogram has value in acute care settings such as the emergency department, operating room and critical care units. We report a novel, hands-free, continuous-wave Doppler ultrasound patch that adheres to the neck and tracks Doppler blood flow metrics in the common carotid artery using an automated algorithm. String and blood-mimicking test objects demonstrated that changes in velocity were accurately measured using both manually and automatically traced Doppler velocity waveforms. In a small usability study with 22 volunteer users (17 clinical, 5 lay), all users were able to locate the carotid Doppler signal on a volunteer subject, and, in a subsequent survey, agreed that the device was easy to use. To illustrate potential clinical applications of the device, the Doppler ultrasound patch was used on a healthy volunteer undergoing a passive leg raise (PLR) as well as on a congestive heart failure patient at resting baseline. The wearable carotid Doppler patch holds promise because of its ease-of-use, velocity measurement accuracy, and ability to continuously record Doppler spectrograms over many cardiac and respiratory cycles.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".