Bibliographic record
Abstract
BACKGROUND: A virtual reality neurosurgery simulator with haptic feedback may help in the training and assessment of technical skills requiring the use of tactile and visual cues. OBJECTIVE: To develop a simulator for craniotomy-based procedures with haptic and graphics feedback for implementation by universities and hospitals in the neurosurgery training curriculum. METHODS: NeuroTouch was developed by a team of more than 50 experts from the National Research Council Canada in collaboration with surgeons from more than 20 teaching hospitals across Canada. Its main components are a stereovision system, bimanual haptic tool manipulators, and a high-end computer. The simulation software engine runs 3 processes for computing graphics, haptics, and mechanics. Training tasks were built from magnetic resonance imaging scans of patients with brain tumors. RESULTS: Two training tasks were implemented for practicing skills with 3 different surgical tools. In the tumor-debulking task, the objective is complete tumor removal without removing normal tissue, using the regular surgical aspirator (suction) and the ultrasonic aspirator. The objective of the tumor cauterization task is to remove a vascularized tumor with an aspirator while controlling blood loss using bipolar electrocautery. CONCLUSION: NeuroTouch prototypes have been set up in 7 teaching hospitals across Canada, to be used for beta testing and validation and evaluated for integration in a neurosurgery training curriculum.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.011 |
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".