Computational fluid dynamics with stents: quantitative comparison with particle image velocimetry for three commercial off the shelf intracranial stents
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Validation of computational fluid dynamics (CFD) in stented intracranial aneurysms (IAs) is still lacking, to reliably predict prone to occlusion hemodynamics, probing, in particular, velocity reduction, and flow pattern changes. This study compares CFD outcome with particle imaging velocimetry (PIV) for three commercial off the shelf (COTS) stents of different material densities. MATERIAL AND METHODS: The recently developed uniform and high precision multi-time lag PIV method was applied to a sidewall aneurysm before and after implantation of three COTS stents with high, intermediate, and low material densities. The measured laser sheet flow patterns and velocity reductions were compared with CFD results and correlated with stent material density. RESULTS: Velocity reduction was in good agreement for unstented high and low porosity stented IA, while flow pattern change was fully matched for unstented and high porosity stented IA. Poor CFD-PIV matching in IA was found for intermediate porosity stents. CONCLUSIONS: CFD reproduced fully PIV measurements in unstented and high porosity stented IAs. With low porosity stents, CFD reproduced velocity reduction and high velocities close to the neck, while a marked mismatch on sluggish flow was found at the dome. CFD was unable to match PIV with intermediate porosity stents for which hemodynamic transition occurred.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 teacher head, 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".