P.103 A spatial analysis of forces applied during virtual reality brain tumor resection: the force pyramid
Bibliographic record
Abstract
Background: Virtual reality simulators allow development of novel methods to analyze neurosurgical performance. Force pyramids provide visual and spatial analysis of 3 dimensional force application by any instrument used during simulated tumor resection. This study was designed to answer three questions: 1) Do study groups have distinct force pyramids? 2) Do handedness and ergonomics influence force pyramid structure? 3) Are force pyramids dependent on visual and haptic characteristics of simulated tumors? Methods: NeuroVR (formerly NeuroTouch), a virtual reality simulator, continually assessed simulated ultrasonic aspirator force application of neurosurgeon, resident and medical student groups during resection of 18 simulated brain tumors with different visual and haptic characteristics. Results: Sixteen neurosurgeons, 15 residents and 84 medical students participated. Neurosurgeon, resident and medical students groups displayed easily distinguishable 3 dimensional ‘force pyramid fingerprints’. Neurosurgeons had the lowest force pyramids, indicating application of the lowest forces, followed by resident and medical student groups. Handedness, ergonomics, visual and haptic tumor characteristics resulted in distinct well-defined 3 dimensional force pyramid patterns. Conclusions: ‘Force pyramid fingerprints’ provide 3 dimensional spatial assessment displays of instrument force application during simulated tumor resections. Neurosurgeon force utilization and ergonomics data form a basis for understanding and modulating resident force application and improving patient safety during tumor resection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".