Design and validation of a hemispherectomy simulator for neurosurgical education
Bibliographic record
Abstract
OBJECTIVE: Early adaptors of surgical simulation have documented a translation to improved intraoperative surgical performance. Similar progress would boost neurosurgical education, especially in highly nuanced epilepsy surgeries. This study introduces a hands-on cerebral hemispheric surgery simulator and evaluates its usefulness in teaching epilepsy surgeries. METHODS: Initially, the anatomical realism of the simulator and its perceived effectiveness as a training tool were evaluated by two epilepsy neurosurgeons. The surgeons independently simulated hemispherotomy procedures and provided questionnaire feedback. Both surgeons agreed on the anatomical realism and effectiveness of this training tool. Next, construct validity was evaluated by modeling the proficiency (task-completion time) of 13 participants, who spanned the experience range from novice to expert. RESULTS: Poisson regression yielded a significant whole-model fit (χ2 = 30.11, p < 0.0001). The association between proficiency when using the training tool and the combined effect of prior exposure to hemispherotomy surgery and career span was statistically significant (χ2 = 7.30, p = 0.007); in isolation, pre-simulation exposure to hemispherotomy surgery (χ2 = 6.71, p = 0.009) and career length (χ2 = 14.21, p < 0.001) were also significant. The mean (± SD) task-completion time was 25.59 ± 9.75 minutes. Plotting career length against task-completion time provided insights on learning curves of epilepsy surgery. Prediction formulae estimated that 10 real-life hemispherotomy cases would be needed to approach the proficiency seen in experts. CONCLUSIONS: The cerebral hemispheric surgery simulator is a reasonable epilepsy surgery training tool in the quest to increase preoperative practice opportunities for neurosurgical education.
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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 | 0.006 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".