Validating a spinal simulation model using NeuroVR
Bibliographic record
Abstract
Introduction: The NeuroTouch/NeuroVR simulator platform is a virtual reality simulator which has been used to compare the performance of expert surgeons to non-experts. Validation of NeuroTouch/NeuroVR is critical to the goal of using this simulator in neurosurgical training, evaluation and curriculum development.Methods: This study was conducted to assess the performance of both neurosurgeon and resident groups performing a left lumbar one level hemilaminectomy using a simulated drill in the dominant hand and simulated suction in the non-dominant hand. Thirteen novel NeuroTouch/NeuroVR derived metrics for spinal simulation were assessed including simple metrics such as: blood loss (BL), percentage of L3 lamina removed (PLR), total tip path length for the drill and suction (TTPL), volume of ligamentum flavum removed (VLFR), sum of forces applied (SFA) of the simulated drill and suction and number of times the thecal sac was touched by an active drill. Other metrics including the suction efficiency index, drill path length index (DPLI) and coordination index (CI) were also assessed. A Likert scale was used to assess the face and content validity of the simulated tasks.The hypotheses tested were: 1) that the novel performance metrics utilized would differentiate neurosurgical performance between neurosurgeon and resident groups and 2) that the simulated task assessed has face, content and construct validity. Results: The simple metrics assessed did not show statistically significant differences in performance between neurosurgeon and resident groups except in the SFA by suction on the ligamentum flavum (neurosurgeons vs junior residents). Advanced metrics showed statistically significant differences in suction efficiency and drill path length indices between the neurosurgeon and senior resident groups. The metrics did not show any significant differences between resident groups. Likert scale evaluation showed the means of overall realism and satisfaction of 3 and 3.5 respectively, and 91.7 % of the participants recommended the use of the simulated task in the training program. Conclusion: The NeuroTouch/NeuroVR platform utilizing the simulated spinal scenario and novel metrics differentiated the performance of expert and non-expert groups. The model demonstrated face and content validity. A number of limitations of the current model and the future improvements needed, are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".