The Impact of Stenosis Severity on Hemodynamic Parameters in the Iliac Artery: A Fluid–Structure Interaction Study
Bibliographic record
Abstract
The common iliac artery supplies blood to the lower extremities, and stenosis in this region severely impacts hemodynamics. This study investigates the effects of 25%, 50%, and 75% iliac artery stenosis on key hemodynamic parameters using a fluid–structure interaction (FSI) approach. Semi-idealized geometries reconstructed from patient-specific data modeled realistic arterial behavior. Parameters such as wall displacement, time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), high oscillatory low shear magnitude (HOLMES) index, and endothelial cell activation potential (ECAP) were evaluated. Results showed peak wall displacement of 2.85 mm in the bifurcation zone under 75% stenosis. TAWSS increased with stenosis severity, peaking in stenotic regions and decreasing significantly downstream. OSI was highest in non-stenosed right branches and bifurcation areas, indicating multidirectional shear forces. HOLMES values were lowest downstream of stenoses, indicating disturbed flow. ECAP exceeded the thrombosis risk threshold (1.4 Pa−1) in post-stenotic zones under 75% stenosis, suggesting a higher risk of clot formation. These results demonstrate that stenosis disrupts local flow and causes hemodynamic changes downstream, emphasizing the need for comprehensive clinical assessment beyond the stenotic site. Regions with elevated ECAP and low HOLMES downstream may be prone to thrombosis, highlighting the importance of careful hemodynamic monitoring for treatment strategies.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".