Inferring the Frank–Starling Curve From Simultaneous Venous and Arterial Doppler: Measurements From a Wireless, Wearable Ultrasound Patch
Bibliographic record
Abstract
The Frank–Starling relationship is a fundamental concept in cardiovascular physiology, relating change in cardiac filling to its output. Historically, this relationship has been measured by physiologists and clinicians using invasive monitoring tools, relating right atrial pressure ( P ra ) to stroke volume (SV) because the P ra -SV slope has therapeutic implications. For example, a critically ill patient with a flattened P ra -SV slope may have low P ra yet fail to increase SV following additional cardiac filling (e.g., intravenous fluids). Provocative maneuvers such as the passive leg raise (PLR) have been proposed to identify these “fluid non-responders”; however, simultaneously measuring cardiac filling and output via non-invasive methods like ultrasound is cumbersome during a PLR. In this Hypothesis and Theory submission, we suggest that a wearable Doppler ultrasound can infer the P ra -SV relationship by simultaneously capturing jugular venous and carotid arterial Doppler in real time. We propose that this method would confirm that low cardiac filling may associate with poor response to additional volume. Additionally, simultaneous assessment of venous filling and arterial output could help interpret and compare provocative maneuvers like the PLR because change in cardiac filling can be confirmed. If our hypothesis is confirmed with future investigation, wearable monitors capable of monitoring both variables of the Frank–Starling relation could be helpful in the ICU and other less acute patient settings.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".