P.016 Bimanual psychomotor performance in neurosurgical resident applicants assessed using NeuroVR (formerly NeuroTouch), a virtual reality simulator
Bibliographic record
Abstract
Background: Current selection methods for neurosurgical residents lack objective measurements of psychomotor performance. This pilot study was designed to answer three questions: 1) What are the differences in bimanual psychomotor performance among neurosurgical residency applicants using the NeuroVR (formerly NeuroTouch) neurosurgical simulator? 2) Are there exceptionally skilled medical student applicants? 3) Does previous surgical exposure influence surgical performance? Methods: Medical students attending neurosurgery residency interviews at McGill University were asked to participate. Participants were instructed to remove 3 simulated brain tumors. Validated tier 1, tier 2, and advanced tier 2 metrics were utilized to assess bimanual psychomotor performance. Demographic data included weeks of neurosurgical elective and prior operative exposure. Results: Sixteen of 17 neurosurgical applicants (94%) participated. Performances clustered in definable top, middle, and bottom groups with significant differences for all metrics. Increased time spent playing music, increase applicant self-evaluated technical skills, high self-ratings of confidence and increased skin closures statistically influenced performance on univariate analysis. A trend for both self-rated increased operating room confidence and increased weeks of neurosurgical exposure to increase blood loss was seen in multivariate analysis. Conclusions: Simulation technology identifies neurosurgical residency applicants at the extremes of technical ability and extrinsic and intrinsic applicant factors appear to influence 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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".