Use of Cobalt–Chromium 64 Wires Surpass Evolve for the Treatment of Remnant Intracranial Aneurysms: A Subanalysis From the SEASE International Registry
Bibliographic record
Abstract
Background The use of the Surpass Evolve flow diverter for intracranial aneurysms (IAs) has shown promising results, but there are no studies that evaluated its effectiveness for remnant previously treated IAs. This study aimed to evaluate the safety and effectiveness of Surpass Evolve for previously treated IAs. Methods This subanalysis is derived from the SEASE (Safety and Effectiveness Assessment of Surpass Evolve) registry, a retrospective, multicentric, international cohort conducted across 15 academic institutions in North America and Europe between July 2020 and October 2022. Adult patients undergoing Surpass Evolve implantation for single saccular IAs were grouped into those with previously treated IAs by coiling or intrasaccular‐devices and those with untreated IAs. Baseline characteristics and outcomes were compared. Results This study included 257 patients with IAs. Of those, 66 patients had previously treated IAs (median time between diagnosis and initial treatment and retreatment: 6.28 months), and 191 patients had untreated IAs. Of the 246 patients with 10.5 months of imaging follow‐up, the core lab adjudicated complete occlusion was less in previously treated IAs compared with untreated IAs (59.7% versus 72.3%; adjusted odds ratio, 0.43 [95% CI, 0.21–0.88]; P = 0.022). Furthermore, we found similar rates of adequate occlusion between previously treated IAs (83.9%) and untreated IAs (82.1%, P = 0.746). There was no difference in rates of major stroke (1.5% versus 2.6%, P >0.999), mortality (1.5% versus 1.0%, P >0.999), modified Rankin Scale score 0–2 (93.7% versus 95.7%, P = 0.504), and retreatment (1.6% versus 3.3%, P = 0.684) between the 2 groups. Conclusion Our results revealed reasonable rates of occlusion and an acceptable safety profile for the use of Surpass Evolve to treat previously treated IAs. Future prospective studies with longer follow‐ups are warranted to explore the findings further.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".