Robotic Long-distance Telementoring in Neurosurgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it