Utilizing NeuroTouch, a virtual reality simulator, to assess and monitor bimanual performance during brain tumor resection
Bibliographic record
Abstract
Background: NeuroTouch simulator provides the potential to determine performance metrics, but validation and easily utilized software are essential before implementation of this platform into neurosurgical training. Objectives: Evaluate and validate neurosurgical performance metrics for simulated brain tumors resection. Develop software and a global web based system to allow utilization of these metrics. Methods: The bimanual resection of 8 simulated brain tumors with differing complexity was evaluated. Software was developed to automatically generate all the metrics from NeuroTouch data output including: blood loss, tumor percentage resected, total brain volume removed, maximum and sum of forces utilized, efficiency index, ultrasonic aspirator path length index (UAPLI), coordination index and ultrasonic aspirator bimanual forces ratio (UABFR). Six neurosurgeons and 12 residents were evaluated. Results: Resident performance was significantly more impaired than neurosurgeon by increasing tumor complexity. Significant differences were found between neurosurgeons, senior, and junior residents on efficiency index and UAPLI. UABFR outlined significant differences between senior and junior residents. Coordination index demonstrated significant differences between junior residents and neurosurgeons. Conclusions: Utilizing metrics employed the NeuroTouch platform differentiated novice from expert performance. Software was developed for metrics and will be made available online for all NeuroTouch users allowing global comparison of neurosurgical performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".