Human–Machine Interface for Robotic Surgery and Stereotaxy
Bibliographic record
Abstract
While considerable technology has been integrated into the operating room, until recently, the actual performance of surgery has seen relatively few changes, relying mainly on hand-eye coordination. This paper outlines the development and composition as well as the requirements and reasoning that lead to the human-machine interface on neuroArm, a telerobotic surgical system. A critical component of the system was the workstation, where information was provided to and received from the operator. The surgeon controls the robotic system using two force-feedback hand controllers based on visual information from a stereoscopic viewing device and two liquid crystal displays. Two touch screens allow the user to monitor and control the settings of the robot and to view and manipulate 3-D MR images. Audio feedback from the surgical site and the operating room staff is also provided by a wireless communication system. The workstation components were chosen not only to recreate the sight, sound, and touch of surgery but also to facilitate the integration of surgeons with advanced imaging and robotic technologies.
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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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".