Simulation-based Education for Endoscopic Third Ventriculostomy: A Comparison Between Virtual and Physical Training Models
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Bibliographic record
Abstract
BACKGROUND: The relative educational benefits of virtual reality (VR) and physical simulation models for endoscopic third ventriculostomy (ETV) have not been evaluated "head to head." OBJECTIVE: To compare and identify the relative utility of a physical and VR ETV simulation model for use in neurosurgical training. METHODS: Twenty-three neurosurgical residents and 3 fellows performed an ETV on both a physical and VR simulation model. Trainees rated the models using 5-point Likert scales evaluating the domains of anatomy, instrument handling, procedural content, and the overall fidelity of the simulation. Paired t tests were performed for each domain's mean overall score and individual items. RESULTS: The VR model has relative benefits compared with the physical model with respect to realistic representation of intraventricular anatomy at the foramen of Monro (4.5, standard deviation [SD] = 0.7 vs 4.1, SD = 0.6; P = .04) and the third ventricle floor (4.4, SD = 0.6 vs 4.0, SD = 0.9; P = .03), although the overall anatomy score was similar (4.2, SD = 0.6 vs 4.0, SD = 0.6; P = .11). For overall instrument handling and procedural content, the physical simulator outperformed the VR model (3.7, SD = 0.8 vs 4.5; SD = 0.5, P < .001 and 3.9; SD = 0.8 vs 4.2, SD = 0.6; P = .02, respectively). Overall task fidelity across the 2 simulators was not perceived as significantly different. CONCLUSION: Simulation model selection should be based on educational objectives. Training focused on learning anatomy or decision-making for anatomic cues may be aided with the VR simulation model. A focus on developing manual dexterity and technical skills using endoscopic equipment in the operating room may be better learned on the physical simulation model.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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