Assessing jugular venous compliance with optical hemodynamic imaging by modulating intrathoracic pressure
Bibliographic record
Abstract
Significance: The internal jugular veins (IJV) are critical cerebral venous drainage pathways that are affected by right heart function. Cardiovascular disease and microgravity can alter central venous pressure (CVP) and venous return, which may contribute to increased intracranial pressure and decreased cardiac output. Assessing jugular venous compliance may provide insight into cerebral drainage and right heart function, but monitoring changes in vessel volume is challenging. Aim: We investigated the feasibility of quantifying jugular venous compliance from jugular venous attenuation (JVA), a noncontact optical measurement of blood volume, along with CVP from antecubital vein cannulation. Approach: CVP was progressively increased through a guided graded Valsalva maneuver, increasing mouth pressure by 2 mmHg every 2 s until a maximum expiratory pressure of 20 mmHg. JVA was extracted from a 1-cm segment between the clavicle and midneck. The contralateral IJV cross-sectional area (CSA) was measured with ultrasound to validate changes in the vessel size. Compliance was calculated using both JVA and CSA between four-beat averages over the duration of the maneuver. Results: JVA and CSA were strongly correlated (median and interquartile range) over the Valsalva maneuver across participants (r = 0.986, [0.983, 0.987]). CVP more than doubled on average between baseline and peak strain (10.7 ± 4.4 vs. 25.8 ± 5.4 cmH2O; p < 0.01). JVA and CSA increased nonlinearly with CVP, and both JVA- and CSA-derived compliance decreased progressively from baseline to peak strain (49% and 56% median reduction, respectively), with no significant difference in compliance reduction between the two measures (Z = - 1.24, p = 0.21). Pressure-volume curves showed a logarithmic relationship in both CSA and JVA. Conclusions: Optical jugular vein assessment may provide new ways to assess jugular distention and cardiac function.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".