A Carotid Doppler Patch Accurately Tracks Stroke Volume Changes During a Preload-Modifying Maneuver in Healthy Volunteers
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Bibliographic record
Abstract
Objectives: Detecting instantaneous stroke volume change in response to altered cardiac preload is the physiologic foundation for determining preload responsiveness. Design: Proof-of-concept physiology study. Setting: Research simulation laboratory. Subjects: Twelve healthy volunteers. Interventions: A wireless continuous wave Doppler ultrasound patch was used to measure carotid velocity time integral and carotid corrected flow time during a squat maneuver. The Doppler patch measurements were compared with simultaneous stroke volume measurements obtained from a noninvasive cardiac output monitor. Measurements and Main Results: From stand to squat, stroke volume increased by 24% while carotid velocity time integral and carotid corrected flow time increased by 32% and 9%, respectively. From squat to stand, stroke volume decreased by 13%, while carotid velocity time integral and carotid corrected flow time decreased by 24% and 10%, respectively. Both changes in carotid velocity time integral and corrected flow time were closely correlated with changes in stroke volume ( r 2 = 0.81 and 0.62, respectively). The four-quadrant plot found a 100% concordance rate between changes in stroke volume and both changes in carotid velocity time integral and changes in corrected flow time. A change in carotid velocity time integral greater than 15% predicted a change in stroke volume greater than 10% with a sensitivity of 95% and a specificity of 92%. A change in carotid corrected flow time greater than 4% predicted a change in stroke volume greater than 10% with a sensitivity of 90% and a specificity of 92%. Conclusions: In healthy volunteers, both carotid velocity time integral and carotid corrected flow time measured by a wireless Doppler patch were useful to track changes in stroke volume induced by a preload-modifying maneuver with high sensitivity and specificity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it