Virtual reality simulation training in stroke thrombectomy centers with limited patient volume—Simulator performance and patient outcome
Bibliographic record
Abstract
BackgroundVirtual reality simulation training may improve the technical skills of interventional radiologists when establishing endovascular thrombectomy at limited-volume stroke centers. The aim of this study was to investigate whether the technical thrombectomy performance of interventional radiologists improved after a defined virtual reality simulator training period. As part of the quality surveillance of clinical practice, we also assessed patient outcomes and thrombectomy quality indicators at the participating centers.MethodsInterventional radiologists and radiology residents from three thrombectomy-capable stroke centers participated in a five months thrombectomy skill-training curriculum on a virtual reality simulator. The simulator automatically registered procedure time, the number of predefined steps that were correctly executed, handling errors, contrast volume, fluoroscopy time, and radiation dose exposure. The design was a before-after study. Two simulated thrombectomy cases were used as pretest and posttest cases, while seven other cases were used for training. Utilizing the Norwegian Stroke Register, we investigated clinical results in thrombectomy during the study period.ResultsNineteen interventional radiologists and radiology residents participated in the study. The improvement between pretest and posttest cases was statistically significant for all outcome measures in both simulated cases, except for the contrast volume used in one case. Clinical patient outcomes in all three centers were well within the recommendations from multi-society consensus guidelines.ConclusionPerformance on the virtual reality simulator improved after training. Virtual reality simulation may improve the learning curve for interventional radiologists in limited-volume thrombectomy centers. No correlation alleged, the clinical data indicates that the centers studied performed thrombectomy in accordance with guideline-recommended standards.
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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 | 0.002 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".