P.034 Evaluation of modeling software for deployment of Pipeline stents in the endovascular treatment of intracranial aneurysms
Bibliographic record
Abstract
Background: Flow diversion is an established endovascular method for the treatment of intracranial aneurysms. The Pipeline Embolization Device (PED) remains the only FDA-approved stent available in USA and Canada since 2011. Stent position plays an important role in determining long-term success. The Leonardo Workstation(Siemens) is used for planning the ideal stent size and post-deployment destination. This first-ever study evaluates the accuracy of modeling software in predicting PED location post-deployment. Methods: 48 PED-assisted cases were performed 2012-2018 at the University of Alberta Hospital. 20 fit our preliminary inclusion criteria (single stents, simple anatomy). The proximal and distal landing zones were used to model the ideal stent using Leonardo. Accuracy was measured by comparing the Leonardo-predicted stent length vs known length. Results modeling against the dimensions predicted by AngioSuite, an app-based interface designed for use in the planning stages. Results: Leonardo workstation is accurate within 5mm at predicting final length for stents oversized by ≥0.25 cm. The predicted difference by Leonardo workstation & AngioSuite did not demonstrate statistical significance (P=0.36, P=0.24 respectively). Conclusions: Current angiographic planning tools are accurate at predicting PED deployment within 5mm. Complex vascular anatomy and deployment of multiple stents make prediction challenging. Analysis of these complex cases is currently underway.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".