Simulation-based Education for Endoscopic Third Ventriculostomy: A Comparison Between Virtual and Physical Training Models
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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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".