P.118 Excalibur, a novel haptic hand-controller for robot-assisted microsurgery
Bibliographic record
Abstract
Background: For robot-assisted telesurgery, the workstation, in particular the haptic handcontroller itself a robot, is paramount to the performance of surgery. Based on the requirements for microsurgery, a novel haptic handcontroller Excalibur has been developed. Methods: Thirty-two surgeons performed a peg-in-hole task (simulating micromanipulation) with Excalibur and two commercially available handcontrollers (Sigma 7 and PHANToM Premium 3.0). A modified Kuka endeffector with bipolar forceps, and Leica microscope completed the remote robotic site. Comparisons were made based on training time, task completion time and number of errors. All participants completed a questionnaire. Results: Repeated measures ANOVA demonstrated significance for task completion time (p=0.004), training time (p=0.021) and number of errors (p=0.004). Surgeons were faster with Excalibur (72s) than with Sigma (96s,p=0.005) and PHANToM (96s,p=0.036). Training time was shorter with Excalibur than with PHANToM (210s vs 310s,p=0.013), and users made fewer errors (0.7 vs 2.1,p=0.008). Training time required for Sigma (285s) and the number of errors (1.3) were not significant. The surgeons found Excalibur smoother, more comfortable, less tiring and easier to maneuver, with more realistic force feedback and superior movement fidelity. Conclusions: Surgical performance was superior with Excalibur compared to the other handcontrollers. This may reflect the microsurgical requirements and unique design architecture of Excalibur.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.015 |
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".