O17/171 World’s first robotic assisted cerebral aneurysm embolization international trial
Bibliographic record
Abstract
Introduction Robotic assisted neurointervention is a recently available technology with exciting future applications in the treatment of neurovascular diseases. Aim of Study To evaluate the effectiveness and safety of robotic platform Corpath GRX (Siemens Healthineers Endovascular Robotics, Newton, MA, USA) for treating cerebral aneurysms alongside 6 months clinical follow-up. Methods This prospective, international, multi-center study enrolled patients with clinical indication for endovascular coil and/or stent-assist coiling embolization. The primary effectiveness was defined as successful completion of the robotic-assisted endovascular procedure absent any unplanned conversion to manual. An independent clinical evaluation committee assessed outcomes gathered over the course of six months post-procedure. Results The study enrolled 117 patients among 10 international sites, with mean age of 56.6 years and 74.4% females. Headache was the most common presenting symptom in 34.2% subjects. Internal Carotid Artery was the most common location (27.9%) and the mean aneurysm height and neck width were 5.7±2.6 mm and 3.5±1.4 mm respectively. Primary effectiveness was achieved in 94% (110/117) subjects with seven subjects requiring conversion to manual procedure. Only 4 primary safety events were recorded with 2 intraprocedural aneurysm ruptures and 2 minor strokes. Raymond Roy Classification Scale of 1 was achieved in 94.1% (96/102) subjects at 6 months follow-up, with mRS of ≤2 for 98.9% (87/88) subjects. Conclusion This trial affirms the efficacy and safety of robotic-assisted cerebral aneurysm treatments, and serves as a steppingstone towards the potential future application of building on the current technological platform for achieving the goal of remote thrombectomy. Disclosure of Interest Vitor Mendes Pereira : Siemens Hal Rice : NOD Laetitia Villiers : NOD Nader Sourour : Balt, Medtronic, Radical Frédéric Clarençon : Artedrone, Stryker, Balt, Medtronic, Intradys Julian Spears : NOD Alejandro Tomasello : NOD Marc Ribo : NOD Vincent Costalat : NOD Gregory Gascou : NOD Pasquale Mordasini : Siemens Jan Gralla : Siemens Mario Martinez Galdamez : NOD Jorge Galván-Fernández : NOD Monika Killer-Oberpfalzer : Siemens Raymond Turner : Medtronic,Microvention,Q’Apel,Integra,Siemens,Endostream,E8,New View Surgical,Viseon Raphael Blanc : Basecamp Vascular BCV Michel Piotin : NOD
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".