Assessing jugular venous compliance with optical hemodynamic imaging by modulating intrathoracic pressure
Bibliographic record
Abstract
Abstract Significance The internal jugular veins 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 non-contact optical measurement of blood volume, alongside 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 mid-neck. Contralateral internal jugular vein cross-sectional area (CSA) was measured with ultrasound to validate changes in 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, 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 cmH 2 O; p <.01). JVA and CSA increased non-linearly 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 =.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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 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".