Correlations Among Indicators of Disturbed Flow at the Normal Carotid Bifurcation
Bibliographic record
Abstract
A variety of hemodynamic wall parameters (HWP) has been proposed over the years to quantify hemodynamic disturbances as potential predictors or indicators of vascular wall dysfunction. The aim of this study was to determine whether some of these might, for practical purposes, be considered redundant. Image-based computational fluid dynamics simulations were carried out for N=50 normal carotid bifurcations reconstructed from magnetic resonance imaging. Pairwise Spearman correlation analysis was performed for HWP quantifying wall shear stress magnitudes, spatial and temporal gradients, and harmonic contents. These were based on the spatial distributions of each HWP and, separately, the amount of the surface exposed to each HWP beyond an objectively-defined threshold. Strong and significant correlations were found among the related trio of time-averaged wall shear stress magnitude (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT). Wall shear stress spatial gradient (WSSG) was strongly and positively correlated with TAWSS. Correlations with Himburg and Friedman's dominant harmonic (DH) parameter were found to depend on how the wall shear stress magnitude was defined in the presence of flow reversals. Many of the proposed HWP were found to provide essentially the same information about disturbed flow at the normal carotid bifurcation. RRT is recommended as a robust single metric of low and oscillating shear. On the other hand, gradient-based HWP may be of limited utility in light of possible redundancies with other HWP, and practical challenges in their measurement. Further investigations are encouraged before these findings should be extrapolated to other vascular territories.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".