User Performance of VR-Based Dissection: Direct Mapping and Motion Coupling of a Surgical Tool
Bibliographic record
Abstract
Robot-assisted surgical systems aim at enhancing surgeon's skills. Nonetheless, the learning curve for mastering such systems is very slow due to the motion-coupling mode that is usually presented in these systems for manipulating a surgical tool. This mode has limitations compared to the direct mapping mode used in open surgery for manipulating a tool. Virtual reality (VR) surgical simulators may reduce the learning time for transferring the surgeon's skills from direct mapping to motion coupling of tool manipulation. This may be accomplished by adding two features to the simulator. First, force models of tool-tissue interaction can be implemented in the haptic interface of the simulator. Second, VR-based surgical tasks can be designed to recreate directmapping mode and motion coupling mode of tool manipulation, as in open and robot-assisted surgeries, respectively. This may permit to transfer the surgeon's skills from open surgery to robotassisted surgery in a timely manner. This work presents a preliminary study on the effect of direct mapping mode and motion coupling mode of tool manipulation on the performance of naïve participants for VR-based brain tissue dissection. An Analytic force model of soft-tissue dissection was implemented in the simulator along with visual feedback of a predefined tool speed of 1 mm/s, which is observed in neurosurgery. The outcomes indicated that the motion quality of the tool via direct mapping was significantly better than with motion coupling. Thus, the study might serve as a first step toward the assessment of user's skills for VR-based robot-assisted dissection.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".