P-018 Treatment of complex intracranial aneurysms using new robotic-assisted techniques: flow diverter and intrasaccular device experience
Bibliographic record
Abstract
Background Robotic stent-assisted coiling has been shown to be a safe and effective treatment for intracranial aneurysms. Purpose We aim to describe our case series of robotic-assisted aneurysm embolizations treated using flow diversion and intrasaccular devices. Results The endovascular robotic neuro-system (Corpath GRX, Siemens Healthineers) can be used to successfully treat various complex intracranial aneurysms in various anatomical locations using flow diversion and intrasaccular devices. Perioperative VasoCT imaging was used to confirm good device placement, including good proximal and distal stent opening, apposition along the parent artery and across the aneurysm neck for flow divertor cases and good aneurysm neck coverage with preservation of distal vessels for intrasaccular device treatments. All clinical procedures were technically successful, with all intracranial steps being performed robotically with no conversions to manual intervention or failures of the robotic system. Conclusions Treatment of intracranial aneurysms using robotic-assisted flow diversion and intrasaccular devices is technically achievable and effective. Disclosures V. Mendes Pereira: 2; C; Corindus. N. Cancelliere: 2; C; Corindus. E. Liu: None. T. Marotta: None. J. Spears: None.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".