Predictors of Aneurysm Obliteration in Patients Treated with the WEB Device: Results of a Multicenter Retrospective Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Despite the numerous studies evaluating the occlusion rates of aneurysms following WEB embolization, there are limited studies identifying predictors of occlusion. Our purpose was to identify predictors of aneurysm occlusion and the need for retreatment. MATERIALS AND METHODS: This is a review of a prospectively maintained database across 30 academic institutions. We included patients with previously untreated cerebral aneurysms embolized using the WEB who had available intraprocedural data and long-term follow-up. RESULTS: < .001), offering a specificity of 87% and a positive predictive value of 85% for long-term occlusion. CONCLUSIONS: Intraprocedural occlusion can be used to predict the chance of long-term aneurysm occlusion and the need for retreatment after embolization with a WEB device. Smoking, aneurysm size, and the presence of an aneurysm wall branch are associated with decreased chances of successful treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".