Widening the Woven EndoBridge: Impact of New Width-Weighted Sizing Method on Woven EndoBridge Selection and Device Compression: A Single Institution Experience in 68 Patients With 12-Month Angiographic Follow-up
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The Woven EndoBridge (WEB) device, developed for wide neck bifurcation aneurysms, is sensitive to appropriate sizing. The instructions for use (IFU) sizing method is based on aneurysm width and dome height. In our institution, we developed a method emphasizing width dimensions to guide WEB selection (width weighted method, WWM) to increase device compression at the neck and consequently flow disruption. We sought to explore the impact of the WWM on WEB compression, angiographic flow disruption, and occlusion rates. METHODS: Consecutive patients with cerebral aneurysms with at least 1 year of follow-up (from February 2019 to December 2023), treated with WEB using the standard IFU sizing method vs the WWM method, were included. Clinical, angiographic, and geometric data were collected and analyzed. RESULTS: A total of 68 aneurysms were included (10 ruptured, 58 unruptured). WEB deployment was technically successful in 67 of 68 aneurysms (98.5%), with complications occurring in 2 patients (2.9%). WWM led to increasing device width dimensions in 35 of 68 cases (51%) with low resizing (2/68, 2.8%) rates. Device compression measured based on WWM was higher compared with theoretical compression based on IFU (41% vs 32%, P < .05). Adequate occlusion (web occlusion scale score 0-2) was achieved in 64 of 68 aneurysms (94.1%) at 1-year follow-up. CONCLUSION: WWM is a structured technique that can lead consistent selection of wider devices to better device compression compared with the IFU-based method. This technique may offer safe and superior flow disruption without an increase in complication rate, potentially translating to higher occlusion rates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".