Parameters of blood viscosity do not correlate with the extent of coronary and carotid atherosclerosis and with endothelial function in patients undergoing coronary angiography
Bibliographic record
Abstract
While the role of physical forces on the control of atherogenesis and the modulation of endothelial function is well known, studies investigating the impact of shear stress on the extent of central atherosclerosis and flow-mediated dilation in humans produced controversial results. We investigated the relationship between viscosity, coronary atherosclerosis, carotid intima-media thickness and flow-mediated dilation in patients undergoing coronary angiography. 451 patients (306 males, mean age 66 ± 10) were enrolled. Viscosity, which was calculated using a validated formula, showed a positive association with platelet activation (P = 0.01), leukocyte counts (P = 0.006) and C-reactive protein (P = 0.03), a marker of inflammation; surprisingly, visocsity showed a negative association with FMD (FMD decreased 0.14 ± 0.05% per each cPoise increase in viscosity) but only in patients without coronary artery disease. Viscosity showed no association with the extent of coronary or carotid artery disease. We provide cross-sectional data on the relationship between whole blood viscosity and parameters of vascular structure and function. While viscosity correlated with parameters of vascular inflammation, it showed no relationship with the presence and severity of central atherosclerosis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".