Remote Surgeon Virtual Presence: A Novel Telementoring Method for Live Surgical Training
Bibliographic record
Abstract
PURPOSE: We describe the first known use of telementoring in corneal surgery and technology combining a 3-dimensional microscope system, 5G live streaming technology, group chat software, and a virtual reality headset for intercontinental surgical supervision. METHODS: Three surgeons in Toronto were proctored by a surgeon in Israel in the implantation of a novel keratoprosthesis device (CorNeat KPro; Ra'anana, Israel) into cadaver eyes. In Toronto, the NGENUITY platform (Alcon) transmitted high-definition, 3-dimensional images to the proctor in Israel who viewed the live video through a GOOVIS Virtual Reality headset with subsecond latency. This was made possible by the LiveU technology (Hackensack, NJ), which is a portable device to increase the bandwidth of transmission. The primary outcome was the successful completion of CorNeat KPro implantation. After each procedure, all surgeons completed a Likert scale questionnaire that assessed opinions on telementoring. RESULTS: All participants implanted the CorNeat KPro device. There was significant satisfaction reported. A total cumulative score from the questionnaire was 149 of 150 from the operating surgeons, with a score of 135 of 150 by the proctor. All felt that there was excellent AV quality with no lag time and recommended the technology. CONCLUSIONS: Telementoring is a promising tool that can traverse large distances for ophthalmic education.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".