Hemodynamic patterns of anterior communicating artery aneurysms: a possible association with rupture
Bibliographic record
Abstract
The aim of this study is to characterize the different flows present at anterior communicating artery (AcoA) aneurysms and investigate possible associations with rupture. For that purpose, patient-specific computational models of 26 AcoA aneurysms were constructed from 3D rotational angiography images. Bilateral images were acquired in 15 patients who had both A1 segments of the anterior cerebral arteries and models were created by fusing the reconstructed left and right arterial trees. Computational fluid dynamics simulations were performed under pulsatile flow conditions. Visualizations of the flow velocity pattern were created to classify the aneurysms into the following flow types: A) inflow from both A1 segments, B) flow jet in the parent artery splits into three secondary jets, one enters the aneurysm and the other two are directed to the A2 segments, C) the parent artery jet splits into two secondary jets, one is directed to one of the A2 segments and the other enters the aneurysm before being directed to the other A2 segment, and D) the parent artery jet enters the aneurysm before being directed towards the A2 segments. The maximum wall shear stress in the aneurysm at the systolic peak (MWSS) was calculated. Most aneurysms in group A were unruptured and had the lowest MWSS. Group B had the same number of unruptured and ruptured aneurysms, and a low MWSS. Groups C and D had high rupture ratios, being the average MWSS significantly higher in group C. Finally, it was found that the MWSS was higher for ruptured aneurysms of all flow types.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".