Quantifying workspace and forces of surgical dissection during robot‐assisted neurosurgery
Bibliographic record
Abstract
BACKGROUND: A prerequisite for successful robot-assisted neurosurgery is to use a hand-controller matched with characteristics of real robotic microsurgery. This study reports quantified data pertaining to the required workspace and exerted forces of surgical tools during robot-assisted microsurgery. METHODS: A surgeon conducted four operations in which the neuroArm surgical system, an image-guided computer-assisted manipulator specifically designed to perform robot-assisted neurosurgery, was employed to surgically remove brain tumors. The position, orientation, and exerted force of surgical tools were measured during operations. RESULTS: Workspace of the neuroArm manipulators, for the cases studied, was 60×60×60 mm(3) while it offered orientation ranges of 103°, 62° and 112°. The surgical tools exerted a maximum force of 1.86 N with frequency band of less than 20 Hz. CONCLUSIONS: This data provides important information specific to neurosurgery that can be used to select among commercially available, or further design a customized, haptic hand-controller for robot-assisted neurosurgical systems. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".