Inferior Vena Caval Measures Do Not Correlate with Carotid Artery Corrected Flow Time Change Measured Using a Wireless Doppler Patch in Healthy Volunteers
Bibliographic record
Abstract
(1) Background: The inspiratory collapse of the inferior vena cava (IVC), a non-invasive surrogate for right atrial pressure, is often used to predict whether a patient will augment stroke volume (SV) in response to a preload challenge. There is a correlation between changing stroke volume (SV∆) and corrected flow time of the common carotid artery (ccFT∆). (2) Objective: We studied the relationship between IVC collapsibility and ccFT∆ in healthy volunteers during preload challenges. (3) Methods: A prospective, observational, pilot study in euvolemic, healthy volunteers with no cardiovascular history was undertaken in a local physiology lab. Using a tilt-table, we studied two degrees of preload augmentation from (a) supine to 30-degrees head-down and (b) fully-upright to 30-degrees head down. In the supine position, % of IVC collapse with respiration, sphericity index and portal vein pulsatility was calculated. The common carotid artery Doppler pulse was continuously captured using a wireless, wearable ultrasound system. (4) Results: Fourteen subjects were included. IVC % collapse with respiration ranged between 10% and 84% across all subjects. Preload responsiveness was defined as an increase in ccFT∆ of at least 7 milliseconds. A total of 79% (supine baseline) and 100% (head-up baseline) of subjects were preload-responsive. No supine venous measures (including IVC % collapse) were significantly related to ccFT∆. (5) Conclusions: From head-up baseline, 100% of healthy subjects were ‘preload-responsive’ as per the ccFT∆. Based on the 42% and 25% IVC collapse thresholds in the supine position, only 50% and 71% would have been labeled ‘preload-responsive’.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".