MS-05 * UTILIZING A VIRTUAL REALITY SIMULATOR, NEUROTOUCH, TO DETERMINE PROFICIENCY PERFORMANCE BENCHMARKS FOR RESECTION OF SIMULATED BRAIN TUMORS
Bibliographic record
Abstract
BACKGROUND: The assessment of neurosurgical technical skills during the resection of cerebral tumors in operative environments is complex. Virtual-reality simulation, have the potential to play important roles in the training of neurosurgical trainees. The purpose of this study was to develop benchmarks for a newly proposed set of objective measures (metrics) of neurosurgical technical skills during simulated brain tumors resection using a new virtual reality simulator (NeuroTouch). METHODS: 33 participants were recruited for this study including 17 experts (Board Certified neurosurgeons), 9 junior and 7 senior neurosurgery residents). Each participant resected 18 simulated brain tumors of varying complexity utilizing the NeuroTouch platform. Metrics for assessing surgical performance consisted of (a) volume of surrounding simulated ‘normal’ brain tissue removed; (b) maximum force applied; (c) sum of forces utilized during tumor resection; (d) percentage of tumor removed; (e) duration for task completion, (f) instrument path lengths; (g) pedal activation frequency. RESULTS: Results demonstrate that neurosurgeons resected less surrounding simulated brain tissue and less tumor then residents consistent with the concept that ‘experts’ focus predominately on surgical safety. Utilizing the trimmed mean method to analyze expert neurosurgical technical skills performance for each different metric, we established benchmarks for proficiency-based training in neurosurgery. Junior and senior residents continually modify their psychomotor performance and that the transition between R4 to R5 training years may be particularly important in acquiring the cognitive input necessary to emphasize patient safety during the resection of brain tumors. CONCLUSION: The safety, quality and efficiency of psychomotor performance of expert and novice operators can be measured using novel metrics derived from the NeuroTouch platform and provides proficiency performance benchmarks for the resection of the simulated brain tumors assessed in this study.
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.
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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".