Prediction of persistent incomplete occlusion of intracranial aneurysms treated with woven EndoBridge device
Bibliographic record
Abstract
While the Woven EndoBridge (WEB) device has transformed the treatment of wide-neck intracranial aneurysms, incomplete occlusion remains a significant challenge requiring better understanding of contributing factors. A retrospective analysis was conducted on multicenter data from patients who underwent WEB device treatment for intracranial aneurysms between January 2011 and December 2022. Using machine learning models, Cox regression, and time-stratified analyses, we evaluated factors associated with persistent incomplete occlusion, defined as non-improving Raymond-Roy Occlusion Classification grade 2 or 3 at final follow-up. Among 813 patients (607 with < 24 months follow-up, 206 with ≥ 24 months), machine learning analysis identified aneurysm height, Acom location, neck diameter, and pretreatment mRS as predictors of persistent incomplete occlusion. On Cox regression. larger aneurysm neck diameter (HR 1.13, 95% CI 1.01-1.27, p = 0.027) and height (HR 1.14, 95% CI 1.02-1.26, p = 0.017), and radial access (HR 2.68, 95% CI 1.76-4.07, p < 0.001) increased, while posterior circulation location (HR 0.56, 95% CI 0.37-0.84, p = 0.005) decreased the risk of persistent incomplete occlusion. Time-stratified analysis revealed that in short-term follow-up (< 24 months), larger aneurysm neck diameter (OR 1.28, 95% CI 1.08-1.52, p = 0.004) increased the risk of incomplete occlusion. In long-term follow-up (≥ 24 months), smoking (OR 2.69, 95% CI 1.04-7.00, p = 0.04), higher pre-treatment mRS (OR 1.78, 95% CI 1.15-2.76, p = 0.009), and immediate flow stagnation (OR 0.33, 95% CI 0.11-0.96, p = 0.04) increased, while older age (OR 0.94, 95% CI 0.90-0.98, p = 0.002) and WEB-DL (OR 0.06, p < 0.001) and SLS devices (OR 0.02, p = 0.003) decreased the risk of persistent incomplete occlusion. Aneurysm characteristics and device type significantly influence long-term WEB treatment outcomes.
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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".