Virtual Reality Tumor Resection: The Force Pyramid Approach
Bibliographic record
Abstract
BACKGROUND: The force pyramid is a novel visual representation allowing spatial delineation of instrument force application during surgical procedures. In this study, the force pyramid concept is employed to create and quantify dominant hand, nondominant hand, and bimanual force pyramids during resection of virtual reality brain tumors. OBJECTIVE: To address 4 questions: Do ergonomics and handedness influence force pyramid structure? What are the differences between dominant and nondominant force pyramids? What is the spatial distribution of forces applied in specific tumor quadrants? What differentiates "expert" and "novice" groups regarding their force pyramids? METHODS: Using a simulated aspirator in the dominant hand and a simulated sucker in the nondominant hand, 6 neurosurgeons and 14 residents resected 8 different tumors using the CAE NeuroVR virtual reality neurosurgical simulation platform (CAE Healthcare, Montréal, Québec and the National Research Council Canada, Boucherville, Québec). Position and force data were used to create force pyramids and quantify tumor quadrant force distribution. RESULTS: Force distribution quantification demonstrates the critical role that handedness and ergonomics play on psychomotor performance during simulated brain tumor resections. Neurosurgeons concentrate their dominant hand forces in a defined crescent in the lower right tumor quadrant. Nondominant force pyramids showed a central peak force application in all groups. Bimanual force pyramids outlined the combined impact of each hand. Distinct force pyramid patterns were seen when tumor stiffness, border complexity, and color were altered. CONCLUSION: Force pyramids allow delineation of specific tumor regions requiring greater psychomotor ability to resect. This information can focus and improve resident technical skills training.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".