Associating Surgeon Feedback With Material Physical Properties in the Development Process of a Resective Epilepsy Surgery Simulator
Bibliographic record
Abstract
BACKGROUND: Hands-on neurosurgical simulations, specifically techniques involving white matter disconnection, are underdeveloped owing to the paucity of low indentation materials that can adequately mimic brain dissection. OBJECTIVE: To describe the discovery phase of developing a resective epilepsy surgery simulator by quantifying the physical properties of 6 materials and correlating the scores with surgeon feedback data. METHODS: Six materials, silicone, TissueMatrix, gel support, Synaptive hydrogel, dry SUP706, and moist SUP706 of equal dimension, were evaluated for hardness by measuring their resistance to indentation. Temporal lobe prototypes, 1 for each material, were dissected by 2 neurosurgeons and ordinal ranking assigned. Two null hypotheses were tested: one is that no differences in the indentation properties of the 6 materials analyzed would be elicited and the other is that there would be no correlation between indentation and surgeon feedback scores. Statistical comparison of the means of the different materials was performed using one-way analysis of variance. Surgeon feedback data and indentation score associations were analyzed using the Kendall rank correlation coefficient. RESULTS: A statistically significant effect (P value <.0001; α 0.05) was measured. Gel support and Synaptive hydrogel had the lowest indentation scores and similar physical properties. Moist support material scored lower than dry support (P = .0067). A strong positive correlation (Kendall tau = 0.9333, P < .0001) was ascertained between the surgeon feedback ranking and indentation scores. CONCLUSION: Reasonable material options for developing a resective epilepsy surgery are proposed and ranked in this article. Early involvement of surgeons is useful in the discovery phase of simulator invention.
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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 | 0.027 |
| 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 | 0.001 | 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".