Abstract 13644: The Effect of Computational Fluid Dynamics Modeling Assumptions in Endothelial Shear Stress Calculations derived From Coronary Computed Tomography Angiography
Bibliographic record
Abstract
Background: Low endothelial shear stress (ESS) is associated with coronary plaque progression and high-risk plaque features. Coronary ESS profiling requires the three-dimensional luminal geometry and Computational Fluid Dynamic (CFD) assumptions to successfully model coronary blood flow. Hypothesis: To examine whether two typical CFD modeling assumptions, namely the exclusion of side branches and the blood model used, alter the determination of those arterial regions that are exposed to low ESS. Methods: Five excised human hearts were imaged with 64-detector row CT. Coronary arteries >1 mm in diameter were segmented and the coronary tree lumen was reconstructed with and without side branches. ESS was calculated for 3 models: a) the entire tree and a non-Newtonian blood model (reference standard) b) the entire tree and a Newtonian blood model and c) major coronaries only (no side branches) and a non-Newtonian blood model (Fig panel A). ESS was compared in each major coronary artery amongst models using Pearson’s correlation and via sensitivity and specificity to detect those 3 mm sections in the coronary circulation that contained the 10% of endoluminal surface with lowest ESS values. For sensitivity and specificity the first model (a) was considered the reference standard. Results: The Newtonian assumption for blood induced minimal change in ESS pattern other than a relative scaling of values. The exclusion of side branches however induced large, coronary-segment specific non-linear changes in both pattern and value. With no side branches, sensitivity and specificity to identify low ESS regions were 78% and 88%. With Newtonian blood and side branches, sensitivity and specificity were 89% and 98% (Fig panel B). Conclusion: Side branches are important for identifying areas of low ESS while the assumption that blood model behaves as a Newtonian fluid has negligible effect.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".