Growth History and Significant Events of Cerebral Aneurysm with Fluid-Structure Interaction Simulations
Bibliographic record
Abstract
We analyzed the growth process of a single cerebral aneurysm using Fluid-Structure Interaction (FSI) simulations establishing a history including important events in morphology, hemodynamics as well as structural mechanics.Data of one patient was obtained between the years 2012 and 2022.The medical imaging data of these aneurysms was provided in the form of digital subtraction angiography, which was transformed into stereolithography format as geometry input.With the FSI simulations typical hemodynamic (wall shear stress, oscillatory shear index) and a structural mechanic quantity (Mises stress) were identified.With the addition of morphological parameters (Size, Volume, L2-norm of Gaussian curvature) significant changes can be found during the growth history of the selected aneurysm.Wall shear stress is highest during aneurysm initiation, while decreasing during aneurysm growth.Oscillatory shear index increases over time especially in the region of strong aneurysm growth.Strong changes in geometry induce subsequent changes in hemodynamics.Wall stress remains constant throughout the growth period of the selected aneurysm.However, shortly before aneurysm rupture wall stress increases significantly.Simulation results of aneurysm growth history can be utilized to identify important events of a growing aneurysm, enabling a better understanding of the behaviour as well as a better estimation for the best time for patient treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".