P-030 Intra-aneurysmal flow diversion assessment using peri-operative advanced imaging after flow diverting stent (FDS) placement: are 64 wires better than 48?
Bibliographic record
Abstract
Background and Purpose Flow diverter devices (FDS) are a true breakthrough in the treatment of neurovascular disease. FDS reduce intra-aneurysmal blood flow inducing progressive thrombosis in a great proportion of treated intracranial aneurysms (IAs). Most of the first generation FDS devices had 48 braided wires, however, next generation devices have 64 wires with better deployment systems. The purpose of this study was to compare intra-aneurysmal flow modifications and flow diversion efficacy between deployments of a newly designed 64-wire (Surpass Evolve; Stryker) and a 48-wire (Pipeline; Medtronic) FDS in various patient-specific silicone aneurysm models. Methods In-vitro experimental set-up using circulating water system and 4 silicone models with internal carotid aneurysms were used. We assessed the intra-aneurysmal flow modification after stent deployment (Evolve vs. Pipeline) using a flow-analysis digital subtraction angiography (DSA) system (AneurysmFlow, Philips Healthcare). The application uses a 3D rotational angiogram (3DRA) and 60 frames/sec DSA runs before and after device deployment to calculate a Mean Aneurysm Flow Amplitude Ratio (MAFA-R). MAFA-R’s are of interest in this experiment as this ratio has previously been shown to be a reliable independent predictor for intracranial aneurysm thrombosis (Pereira et al., 2012). A total of 8 devices were deployed in four different silicone models. For each experimental model, we randomly selected either a Pipeline or Evolve stent to deploy first, followed by removal and deployment of the other stent. Intra-aneurysmal flow calculations were performed before and after each stent deployment and compared using a paired t-test. A VasoCT was performed to confirm suitable stent apposition to the arterial wall. Results Average MAFA-Ratio values calculated from pre- and post-stent placement were significantly lower after deployment of the 64-wire device (mean= 0.62±0.09) compared to the 48-wire device (0.71±0.06); p=<0.05. Conclusions Our in-vitro results show that the 64 wire FDS (Evolve) had superior flow diversion effect compared to the 48 wire FDS (Pipeline), suggesting that 64-wires are superior to 48-wire designs for flow diversion efficacy. Disclosures N. Cancelliere: None. P. Nicholson: None. K. Mendes: None. E. Orru: None. T. Krings: None. V. Mendes Pereira: 1; C; Philips Healthcare. 2; C; Stryker, Medtronic.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".