Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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