Effects of aspect ratio, wall thickness and hypertension in the patient-specific computational modeling of cerebral aneurysms using fluid-structure interaction analysis
Bibliographic record
Abstract
Intracranial aneurysm is a pathological dilatation of the cerebral artery which can lead to high mortality rate upon rupture. The aspect ratio (AR) of an aneurysm, being the ratio of the height to neck width, is an important factor in estimating the likelihood of aneurysm rupture in clinical practice. AR will generally increase while the aneurysm grows. Clinical observations over the years show that aneurysms with larger AR usually exhibit higher rupture risk. The goal of the current study is to conduct Fluid-Structure Interaction (FSI) analyses to provide quantitative estimates on the importance of AR, wall thickness (tw) and hypertension. The effects of varying AR and tw on the hemodynamics, wall stress and displacement will be studied based on patient-specific models. Both sidewall and bifurcation aneurysms are investigated. There is a significant increase in the wall stress at the aneurysmal dome (the location in an aneurysm where rupture is commonly observed clinically) when the AR increases and tw decreases due to the aneurysm growth process. Furthermore, these investigations are repeated for patients with hypertension (high blood pressure) conditions. The increase in the wall stress due to hypertension for models with higher ARs is more dramatic. The clinically observed feature of higher rupture risk of aneurysms with larger AR is thus supported quantitatively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".