Merging machines with microsurgery: clinical experience with neuroArm
Bibliographic record
Abstract
OBJECT: It has been over a decade since the introduction of the da Vinci Surgical System into surgery. Since then, technology has been advancing at an exponential rate, and newer surgical robots are becoming increasingly sophisticated, which could greatly impact the performance of surgery. NeuroArm is one such robotic system. METHODS: Clinical integration of neuroArm, an MR-compatible image-guided robot, into surgical procedure has been developed over a prospective series of 35 cases with varying pathology. RESULTS: Only 1 adverse event was encountered in the first 35 neuroArm cases, with no patient injury. The adverse event was uncontrolled motion of the left neuroArm manipulator, which was corrected through a rigorous safety review procedure. Surgeons used a graded approach to introducing neuroArm into surgery, with routine dissection of the tumor-brain interface occurring over the last 15 cases. The use of neuroArm for routine dissection shows that robotic technology can be successfully integrated into microsurgery. Karnofsky performance status scores were significantly improved postoperatively and at 12-week follow-up. CONCLUSIONS: Surgical robots have the potential to improve surgical precision and accuracy through motion scaling and tremor filters, although human surgeons currently possess superior speed and dexterity. Additionally, neuroArm's workstation has positive implications for technology management and surgical education. NeuroArm is a step toward a future in which a variety of machines are merged with medicine.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".