Treatment of Unruptured Small and Medium‐Sized Wide Necked Aneurysms Using the 64‐Wire Surpass Evolve: A Subanalysis From the SEASE International Registry
Bibliographic record
Abstract
Background Flow diversion has revolutionized the management of wide‐necked intracranial aneurysms (IAs). We aimed to assess the effectiveness and safety of the new generation 64‐wire Surpass Evolve for the treatment of unruptured small/medium‐sized IAs. Methods and Results This is a subanalysis from the SEASE (Safety and Effectiveness Assessment of the Surpass Evolve) registry, an observational cohort study including 15 academic institutions in North America and Europe between July 2020 and October 2022. Patients with wide‐necked saccular IAs, measuring <12 mm along the internal carotid artery and vertebrobasilar system, and treated with the Surpass Evolve were included. Primary effectiveness was complete occlusion (Raymond‐Roy class 1) at follow‐up (core laboratory adjudicated), and primary safety was major stroke (ischemic/hemorrhagic) in the territory supplied by the target artery or death. A total of 129 cases with 135 IAs were included (median age 59 years, 85.3% women). Median maximum IAs size and neck size were 5.1 and 3.9 mm, respectively. Most IAs were in the internal carotid artery C6 (65.9%, 89/135) and C7 (14.1%, 19/135) segments. At a median follow‐up time of 10.2 months (interquartile range, 6.4–12.8), complete occlusion was 77.1% (101/131), ≥50% in‐stent stenosis was 8.8% (11/125), and retreatment was 0.8% (1/125). Major stroke and mortality were reported in 2 (1.6%) patients and 1 (0.8%) patient, respectively. Size was the only factor associated with higher odds of incomplete occlusion (adjusted odds ratio, 1.2 [95% CI, 1.02–1.5]; P =0.03). Conclusions Patients with small/medium‐sized IAs can be effectively treated using the Surpass Evolve, a new generation, 64‐wire, cobalt‐chromium flow diverter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".