P.127 Hemodynamic factors of internal carotid artery blister aneurysms: role of the Wall Shear Stress Distribution
Bibliographic record
Abstract
Background: The pathophysiology of internal carotid artery (ICA) blister aneurysms is poorly understood. Our goal is to investigate the hemodynamic factors contributing to their formation and progression using computational fluid dynamics. Methods: We developed software allowing 3D reconstruction of type I and II blister aneurysms (Bojanowski et al., 2015) from ICA angiography. Kinematic blood flow data was obtained using a finite volume solver. We compared the wall shear stress distribution (WSS) of the healthy arterial wall under various blood pressure conditions. Results: WSS was maximal on the dorsal wall of the supraclinoid segment of the ICA at the distal part of the future site of the aneurysm sac, suggesting that the aneurysm sac initially develops in a retrograde fashion. The WSS gradient (WSSG) was maximal at both the proximal and distal boundaries of the bulging aneurysm. Hypertension exponentially exacerbates the WSS distribution. Very low WSS associated with a high WSSG at the proximal part of the aneurysm sac could explain the extension of the hemorrhage proximal to the forming blister. Conclusions: WSS and its gradient participate in the formation and progression of blister aneurysms of the supraclinoid segment of the ICA. Increasing blood pressure contributes exponentially to the formation of blister aneurysms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".