Numerical investigation of unruptured middle cerebral artery bifurcation aneurysms: influence of aspect ratio
Bibliographic record
Abstract
An aneurysm is a disease condition, which is due to the pathological weakening of an arterial wall. These aneurysms are often found in various branch points and bifurcations of an artery in the cerebral circulation. Most aneurysms come to medical attention, either due to brain hemorrhages caused by rupture or found unruptured. To consider surgically invasive treatment modalities, clinicians need scientific methods such as, hemodynamic analysis to assess rupture risk. The arterial wall loses its structural integrity when wall shear stress (WSS) and other hemodynamic parameters exceed a certain threshold. In the present study, numerical simulations are carried out for unruptured middle cerebral artery (MCA) aneurysms. Three distinct representative sizes are chosen from a larger patient pool of 26 MCA aneurysms. Logically, these aneurysms represent three growth stages of any patient with similar anatomical structure. Simulations are performed to compare the three growth phases (with different aspect ratios) of an aneurysm and correlate their hemodynamic parameters. Simulations with patient specific boundary conditions reveal that, aneurysms with a higher aspect ratio (AR) correspond to an attendant decrease in both time-averaged wall shear stress (TAWSS) and spatial wall shear stress gradients (WSSG). Smaller MCAs were observed to have higher positive wall shear stress divergence (WSSD), exemplifying the tensile nature of arterial wall stretching. Present study identifies positive wall shear stress divergence (PWSSD) to be a potential biomarker for evaluating the growth of an aneurysm.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".