User Performance of VR-Based Tissue Dissection Under the Effects of Force Models and Tracing Speeds
Bibliographic record
Abstract
Significant research efforts have been devoted to the development of force models that estimate soft-tissue biomechanical responses, finding an application on virtual reality (VR) based surgery training simulation. Nonetheless, the effects of force models on user performance of surgical tasks at different translation speeds are yet unclear. Thus, this work evaluated the effects of simple Weibull and realistic Analytic force models on 10 naïve human subjects for performing 1 degree-of-freedom (DOF) brain-tissue dissection tasks on a VR simulator at speeds of 0.10, 1.27, and 2.54 cm/s. Relying on 4 objective and 5 subjective performance metrics, two-way and one-way ANOVA analyses showed that a realistic force model such as the Analytic model is required to lessen the workload perceived by users only at a low dissection speed of 0.10 cm/s like that observed in neurosurgery. It was also found that dissections performed at the speed of 0.10 cm/s demand more refined manual skills than those at higher speeds. This finding complies with the lengthy surgery training curricula required to master surgical skills.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".