PROFICIENCY PERFORMANCE BENCHMARKS FOR REMOVAL OF SIMULATED BRAIN TUMORS USING NEUROTOUCH
Bibliographic record
Abstract
BACKGROUND: Objective assessment of neurosurgical technical skills involved in the resection of cerebral tumors in operative environments is complex. Educators emphasize the need to develop and use objective and meaningful assessment tools that are reliable and valid for assessing trainees' progress in acquiring surgical skills. Novel technologies, such as virtual-reality simulation, have the potential to play important roles in the training of neurosurgeons. The purpose of this study was to develop benchmarks for a newly proposed set of objective measures (metrics) of neurosurgical technical skills performance during simulated brain tumor resection using a new virtual reality simulator (NeuroTouch). METHODS: A total of 31 participants were recruited including 16 ‘experts’ (neurosurgery staff) and 15 neurosurgery residents (‘novices’, 7 junior and 8 senior). Each participant performed 18 simulated brain tumor resections utilizing the NeuroTouch platform. The metrics for assessing surgical performance were computed using the NeuroTouch simulator and consisted of 1) Safety metrics including, volume of surrounding normal tissue removed, maximum force applied and sum of forces utilized during tumor resection 2) Quality of Operation metric which involved the percentage of tumor removed and 3) Efficiency metrics including duration for task completion, instrument path lengths and pedal activation frequency. RESULTS: The results demonstrated that ‘expert’ neurosurgeons (neurosurgery staff) resected less surrounding simulated normal brain tissue and less tumor tissue then residents. This data is consistent with the concept that ‘experts’ focused more on safety of the surgical procedure compared to novices. By analyzing experts' neurosurgical technical skills performance on these different metrics we were able to establish benchmarks for goal proficiency-based training of neurosurgery residents. CONCLUSIONS: Examining ‘expert’ neurosurgical performance in simulated settings such as NeuroTouch provides researchers with novel metrics for assessment of technical skills and development of proficiency based benchmarks. Identification of expert proficiency can led to improvements in resident training and assessment SECONDARY CATEGORY: n/a.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Open science | 0.001 | 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".