Robotic Long-distance Telementoring in Neurosurgery
Bibliographic record
Abstract
OBJECTIVE: To test the feasibility of long-distance telementoring in neurosurgery by providing subspecialized expertise in real time to another neurosurgeon performing a surgical procedure in a remote location. METHODS: A robotic telecollaboration system (Socrates; Computer Motion, Inc., Santa Barbara, CA) capable of controlling the movements of a robotic arm, of handling two-way video, and of audio communication as well as transmission of neuronavigational data from the remote operating room was used for the telementoring procedures. Four integrated services digital network lines with a total speed of transmission of 512 kilobytes per second provided telecommunications between a large academic center (Halifax, Nova Scotia) and a community-based center (Saint John, New Brunswick) located 400 km away. RESULTS: Long-distance telementoring was used in three craniotomies for brain tumors, a craniotomy for an arteriovenous malformation, a carotid endarterectomy, and a lumbar laminectomy. There were no surgical complications during the procedures, and all patients had uneventful outcomes. The neurosurgeons in the remote location believed that the input from the mentors was useful in all of the cases and was crucial in the removal of a mesial temporal lobe glioma and resection of an occipital arteriovenous malformation. CONCLUSION: Our initial experience with long-distance robotic-assisted telementoring in six cases indicates that telementoring is feasible, reliable, and safe. Although still in its infancy, telementoring has the potential to improve surgical care, to enhance neurosurgical training, and to have a major impact on the delivery of neurosurgical services throughout the world.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".