Efficacy of LVIS vs. Enterprise Stent for Endovascular Treatment of Medium-Sized Intracranial Aneurysms: A Hemodynamic Comparison Study
Bibliographic record
Abstract
Background: We conducted a computational fluid dynamics (CFD) study, compared the treatment of medium-sized intracranial aneurysms with LVIS and Enterprise stent assisted coil embolization (SACE) to determine the effects of hemodynamic changes caused by different stent and coil packing densities (PD) in endovascular treatment. Methods: We enrolled 87 consecutive patients, with 87 medium-sized intracranial aneurysms (≥7 mm, ≤12 mm), who underwent LVIS or Enterprise SACE. Aneurysms treated with LVIS SACE were allocated to the LVIS group, and the remainder to the Enterprise group. CFD were performed to assess hemodynamic alterations between before treatment, after stent deployment, and after SACE. Results: One aneurysm recanalized in the LVIS group (n=42) and five recanalized in the Enterprise group (n=45) (recanalization rate: 2.4% vs 11.1%, respectively; P=0.108). Higher complete obliteration rate (P=0.069) and PD (P=0.146) was found in the LVIS group. Velocity at the neck plane showed a greater reduction ratio than velocity and WSS of the aneurysm in both groups after stent deployment, while velocity and WSS of the aneurysm showed a greater reduction ratio after coil placement. Further, there was a greater reduction in velocity at the neck plane (59.52% vs 39.81%), aneurysmal velocity (88.46% vs 69.45%), and wall shear stress (WSS) (85.45% vs 69.49%) on the aneurysm in the LVIS group (P<0.001 for all). Specifically, the reduction ratio of velocity at the neck plane showed significant difference between the groups in the multivariate analysis (p=0.013). Conclusions: LVIS SACE showed a lower recanalization for endovascular treatment of medium-sized intracranial aneurysms, and the greater hemodynamic alterations might be the key factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".