Branching and nonbranching intracranial aneurysms in the presence of a persistent stapedial artery and an aberrant internal carotid artery assessed with computational hemodynamics: illustrative case
Bibliographic record
Abstract
BACKGROUND: The mechanisms underlying the initiation and progression of bifurcation versus lateral wall aneurysms are not well understood. Computational fluid dynamics (CFD) can improve the understanding of these mechanisms and can consequently help identify patients at higher risk for developing aneurysms and monitor them more closely. OBSERVATIONS: A 36-year-old man presented with a ruptured anterior communicating artery aneurysm, which was successfully treated with microsurgical clipping. Imaging also revealed a persistent stapedial artery with an elongated and tortuous posterior communicating artery (PComA). Fourteen years later, he was readmitted for a ruptured aneurysm on a PComA loop. CFD helped identify considerable collateral circulation due to the aberrant internal carotid artery (ICA). High flow rates trigger both types of aneurysms, but nuances exist in the hemodynamic mechanisms that drive their growth. LESSONS: Berry aneurysms and lateral wall aneurysms initially start due to a high flow rate, a common underlying cause. However, the formation of true sidewall aneurysms is primarily characterized by locally increased wall shear stress, while the development of berry aneurysms is mainly linked to high local blood pressure at arterial bifurcations. An aberrant ICA can lead to supraphysiological compensatory flow in the anterior and posterior circulation, increasing the risk of intracranial aneurysm formation at both branching and nonbranching sites, underscoring the need for lifelong monitoring. https://thejns.org/doi/10.3171/CASE24421.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".