Using robotics to move a neurosurgeon’s hands to the tip of their endoscope
Bibliographic record
Abstract
A major advantage of surgical robots is that they can reduce the invasiveness of a procedure by enabling the clinician to manipulate tools as they would in open surgery but through small incisions in the body. Neurosurgery has yet to benefit from this advantage. Although clinical robots are available for the least invasive neurosurgical procedures, such as guiding electrode insertion, the most invasive brain surgeries, such as tumor resection, are still performed as open manual procedures. To investigate whether robotics could reduce the invasiveness of major brain surgeries while still providing the manipulation capabilities of open surgery, we created a two-armed joystick-controlled endoscopic robot. To evaluate the efficacy of this robot, we developed a set of neurosurgical skill tasks patterned after the steps of brain tumor resection. We also created a patient-derived brain model for pineal tumors, which are located in the center of the brain and are normally removed by open surgery. In comparison, testing with existing manual endoscopic instrumentation, we found that the robot provided access to a much larger working volume at the trocar tip and enabled bimanual tasks without compression of brain tissue adjacent to the trocar. Furthermore, many tasks could be completed faster with the robot. These results suggest that robotics has the potential to substantially reduce the invasiveness of brain surgery by enabling certain procedures currently performed as open surgery to be converted to endoscopic interventions.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".