Surgical Robotics: A Review and Neurosurgical Prototype Development
Bibliographic record
Abstract
PURPOSE: The purpose of this article is to update the neurosurgical community on the expanding field of surgical robotics and to present the design of a novel neurosurgical prototype. It is intended to mimic standard technique and deploy conventional microsurgical tools. The intention is to ease its integration into the "nervous system" of both the traditional operating room and surgeon. CONCEPT: To permit benefit from updated intraoperative imaging, magnetic resonance imaging-compatible materials were incorporated into the design. Advanced haptics, optics, and auditory communication with the surgical site recreate the sight, sound, and feel of neurosurgery. RATIONALE: Magnification and advanced imaging have pushed surgeons to the limit of their dexterity and stamina. Robots, in contrast, are indefatigable and have superior spatial resolution and geometric accuracy. The use of tremor filters and motion scalers permits procedures requiring superior dexterity. DISCUSSION: Breadboard testing of the prototype components has shown spatial resolution of 30 microm, greatly exceeding our expectations. Neurosurgeons will not only be able to perform current procedures with a higher margin of safety but also must speculate on techniques that have hitherto not even been contemplated. This includes coupling the robot to intelligent tools that interrogate tissue before its manipulation and the potential of molecular imaging to transform neurosurgical research into surgical exploration of the cell, not the organ.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".