Stent-Induced Vascular Remodeling in Two-Step Stent-Assisted Coiling Treatment of Brain Aneurysms: A Closer Look Into the Hemodynamic Changes During the Stent Healing Period
Bibliographic record
Abstract
Stenting has become an important adjunctive tool for assisting coil embolization in complex-shaped intracranial aneurysms. However, as a secondary effect, stent deployment has been related to both immediate and delayed remodeling of the local vasculature. Recent studies have demonstrated that this phenomenon may assume different roles depending on the treatment stage. However, the extent of such event on the intra-aneurysmal hemodynamics is still unclear; especially when performing two-step stent-assisted coiling (SAC). Therefore, we performed computational fluid dynamics (CFD) analysis of the blood flow in four bifurcation aneurysms focusing on the stent healing period found in SAC as a two-step maneuver. Our results show that by changing the local vasculature, the intra-aneurysmal hemodynamics changes considerably. However, even though changes do occur, they were not consistent among the cases. Furthermore, by changing the local vasculature not only the shear levels change but also the shear distribution on the aneurysm surface. Additionally, a geometric analysis alone can mislead the estimation of the novel hemodynamic environment after vascular remodeling, especially in the presence of mixing streams. Therefore, although the novel local vasculature might induce an improved hemodynamic environment, it is also plausible to expect that adverse hemodynamic conditions might occur. This could pose a particularly delicate condition since the aneurysm surface remains completely exposed to the novel hemodynamic environment during the stent healing period. Finally, our study emphasizes that vascular remodeling should be considered when assessing the hemodynamics in aneurysms treated with stents, especially when evaluating the earlier stages of the treatment process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".