Defining the physiological bounds of left ventricular ejection time with a wireless, wearable ultrasound: An analysis of over 137,000 cardiac cycles
Bibliographic record
Abstract
Background: Flow time (FT) or the left ventricular ejection time (LVET) is the duration of mechanical systole, when the aortic valve is open and ejecting blood. LVET can be measured in the common carotid artery from the time of the systolic upstroke to the incisural notch. FT is directly related to stroke volume (SV) and therefore has important implications for inpatient and outpatient cardiovascular care. Despite this known relationship between FT (i.e., LVET) and SV, large patient datasets describing the distribution and physiological bounds of FT are lacking. Methods: Using a wearable, continuous-wave Doppler ultrasound patch, we are amassing a database of cardiac cycles from the common carotid Doppler pulse in patients and healthy volunteers performing various preload challenges. Results: From this dataset of over 137,000 measurements in 347 patients, we report the mean and distributions of the common carotid artery flow time (i.e., LVET) corrected for heart rate using several prevailing equations. Conclusions: Our findings are the most extensive exploration of the physiological bounds of FT (i.e., LVET) and are useful in both clinical assessments of cardiac health and various algorithm detection applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".