Carotid Artery Corrected Flow Time Measured by Wearable Doppler Ultrasound Accurately Detects Changing Stroke Volume During the Passive Leg Raise in Ambulatory Volunteers
Bibliographic record
Abstract
Background: The change in the corrected flow time of the common carotid artery (ccFTΔ) has been used as a surrogate of changing stroke volume (SVΔ) in the critically-ill. Thus, this relatively easy-to-obtain Doppler measure may help clinicians better define the intended effect of intravenous fluids. Yet the temporal evolution of SVΔ and ccFTΔ has not been reported in volunteers undergoing a passive leg raise (PLR). Methods: We recruited clinically-euvolemic, non-fasted, adult, volunteers in a local physiology lab to perform 2 PLR maneuvers, each separated by a 5 minute ‘wash-out’. During each PLR, SV was measured by a non-invasive pulse contour analysis device. SV was temporally-synchronized with a wireless, wearable Doppler ultrasound worn over the common carotid artery that continuously measured ccFT. Results: 36 PLR maneuvers were obtained across 19 ambulatory volunteers. 8856 carotid Doppler cardiac cycles were analyzed. The ccFT increased nearly ubiquitously during the PLR and within 40–60 seconds of PLR onset; the rise in SV from the pulse contour device was more gradual. SVΔ by +5% and +10% were both detected by a +7% ccFTΔ with sensitivities, specificities and areas under the receiver operator curve of 59%, 95% and 0.77 (p < 0.001) and 66%, 76% and 0.73 (p < 0.001), respectively. Conclusions: The ccFTΔ during the PLR in ambulatory volunteers was rapid and sustained. Within the limits of precision for detecting a clinically-significant rise in SV by a non-invasive pulse contour analysis device, simultaneously-acquired ccFT from a wireless, wearable ultrasound system was accurate at detecting ‘preload responsiveness’.
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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 | 0.003 |
| 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) | 0.001 | 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".