Metric based virtual simulation training for endovascular thrombectomy improves interventional neuroradiologists’ simulator performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it