Defining ideal middle cerebral artery bifurcation aneurysm size for Woven EndoBridge embolization
Bibliographic record
Abstract
OBJECTIVE: The Woven EndoBridge (WEB) device was approved to treat wide-necked bifurcation aneurysms. The device is designed as an intrasaccular flow disruptor covering aneurysm widths up to 10 mm. Although prior studies combined all aneurysm sizes, it is known that aneurysms behave differently in response to endovascular treatment based on their size. Therefore, the authors' objective was to identify ideal middle cerebral artery (MCA) aneurysm width and neck sizes most suitable for WEB treatment. METHODS: The WorldWideWEB consortium is a large multicenter retrospective database that analyzes intracranial aneurysms treated with the WEB device. In this study, all unruptured MCA bifurcation aneurysms with available measurements were included. Cutoff values based on aneurysm width and neck in relation to aneurysm occlusion status were measured using the receiver operating characteristic (ROC) curve. Propensity score matching (PSM) was then used to compare treatment outcomes between aneurysms smaller and larger than the cutoff value for both width and neck size. RESULTS: The ideal cutoff values for MCA bifurcation aneurysm width and neck were 6.1 mm and 4.6 mm, respectively. On PSM, 87 matched pairs were compared based on width size (≤ 6.1 mm and > 6.1 mm), and 77 matched pairs were compared based on neck size (≤ 4.6 mm and > 4.6 mm). There was a significant difference in adequate aneurysm occlusion between aneurysms smaller and larger than those cutoff values for both widths (93% vs 76%, p = 0.0017) and neck sizes (90% vs 70%, p = 0.0026). The retreatment rate was also significantly higher for larger aneurysms in both parameters. CONCLUSIONS: This study shows that MCA bifurcation aneurysms ≤ 6.1 mm in width and ≤ 4.6 mm in neck size are significantly better candidates for WEB treatment, leading to improved occlusion status and reduced retreatment rate, which are important considerations when using WEB devices.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".