Metric based virtual simulation training for endovascular thrombectomy improves interventional neuroradiologists’ simulator performance
Bibliographic record
Abstract
OBJECTIVE: Metric based virtual reality simulation training may enhance the capability of interventional neuroradiologists (INR) to perform endovascular thrombectomy. As pilot for a national simulation study we examined the feasibility and utility of simulated endovascular thrombectomy procedures on a virtual reality (VR) simulator. METHODS: Six INR and four residents participated in the thrombectomy skill training on a VR simulator (Mentice VIST 5G). Two different case-scenarios were defined as benchmark-cases, performed before and after VR simulator training. INR performing endovascular thrombectomy clinically were also asked to fill out a questionnaire analyzing their degree of expectation and general attitude towards VR simulator training. RESULTS: All participants improved in mean total procedure time for both benchmark-cases. Experts showed significant improvements in handling errors (case 2), a reduction in contrast volume used (case 1 and 2), and fluoroscopy time (case 1 and 2). Novices showed a significant improvement in steps finished (case 2), a reduction in fluoroscopy time (case 1), and radiation used (case 1). Both, before and after having performed simulation training the participating INR had a positive attitude towards VR simulation training. CONCLUSION: VR simulation training enhances the capability of INR to perform endovascular thrombectomy on the VR simulator. INR have generally a positive attitude towards VR simulation training. Whether the VR simulation training translates to enhanced clinical performance will be evaluated in the ongoing Norwegian national simulation study.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".