Virtual and analytical self-expandable braided stent treatment models
Bibliographic record
Abstract
The Flow Diverter is a self-expandable braided stent that has helped improve the effectiveness of cerebral aneurysm treatment during the last decade. The Flow Diverter's efficiency heavily relies on proper decision-making during the pre-operative phase, which is currently based on static measurements that fail to account for vessel or tissue deformation. In the context of providing realistic measurements, a biomechanical computational method is designed to aid physicians in predicting patient-specific treatment outcomes. The method integrates virtual and analytical treatment models, validated against experimental mechanical tests, and two patient treatment outcomes. In the case of both patients, deployed stent length was one of the validated result parameters, which displayed an error inferior to 1.5% for the virtual and analytical models. These results indicated both models' accuracy. However, the analytical model provided more accurate results with a 0.3% error while requiring a lower computational cost for length prediction. This computational method can offer designing and testing platforms for predicting possible intervention-related complications, patient-specific medical device designs, and pre-operative planning to automate interventional procedures.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".