Safety and effectiveness assessment of the surpass evolve (SEASE): a post-market international multicenter study
Bibliographic record
Abstract
BACKGROUND: Flow diverters are the first-line treatment for specific intracranial aneurysms (iA). Surpass Evolve (SE) is a new-generation 64-wire flow diverter with a high braid angle. Current literature on the SE is limited. We aimed to report the first international real-world experience evaluating the safety and effectiveness of the SE. METHODS: The Safety and Effectiveness Assessment of the Surpass Evolve (SEASE) was a multicenter retrospective international post-marketing cohort study including consecutive patients treated with SE for iAs between 2020 and 2022. Demographic, clinical, and angiographic data were collected. Primary effectiveness was independent core lab adjudicated complete occlusion rates (Raymond-Roy Class 1) at last follow-up. Primary safety were major ischemic/hemorrhagic events and mortality. RESULTS: In total, 305 patients with 332 aneurysms underwent SE implantation. The patients had a median age of 59 [50-67] years, and 256 (83.9%) were female. The baseline modified Rankin scale score was 0-2 in 291 patients (96.7%). Most aneurysms were unruptured (285, 93.4%) and saccular (309, 93.1%). Previous treatment was present in 76 (22.9%) patients. The median aneurysm size was 5.1 [3.4-9.0] mm, and the median neck width was 3.6 [2.7-5.1] mm. Most aneurysms were in the internal carotid artery C6 ophthalmic segment (126, 38.0%), followed by the communicating segment (58, 17.5%). At median 10.2 [6.4-12.9] months follow-up, 233 (73.0%) aneurysms achieved complete occlusion. After adjusting for confounders, complete occlusion remained consistent. Major stroke and procedure-related mortality were reported in 6 (2%) and 2 (0.7%) cases, respectively. CONCLUSION: These results demonstrate that SE has a consistently high effectiveness and favorable safety for the treatment of iAs.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".