Digital Video Capture and Synchronous Consultation in Open Surgery
Bibliographic record
Abstract
In Brief Objective: To achieve real-time or simultaneous surgical consultation and education to students in distant locations, we report the successful integration of robotics, video-teleconferencing, and intranet transmission using currently available hardware and Internet capabilities. Summary Background Data: Accurate visualization of the surgical field with high-resolution video imaging cameras such as the closed-coupled device (CCD) of the laparoscope can serve to insure clear visual observation of surgery and share the surgical procedure with trainees and, or consultants in a distant location. Prior work has successfully applied optics and technical advances to achieve precise visualization in laparoscopy. Methods: Twenty-five thyroidectomy explorations in 15 patients were monitored and transmitted bidirectionally with audio and video data in real-time. Remotely located surgical trainees (n = 4) and medical students (n = 3) confirmed 7 different anatomic landmarks during each surgical procedure. The study used the Socrates System (Computer Motion, Inc. [CMI], Goleta, CA), an interactive telementoring system inclusive of a telestration whiteboard, in conjunction with the AESOP robotic arm and Hermes voice command system (CMI). A 10-mm flat laparoscopic telescope was used to capture the optical surgical field. As voice, telestrator, or marker confirmed each anatomic landmark the image parameters of resolution, chroma (light position and intensity), and luminance were assessed with survey responses. Results: Confirmation of greater than 90% was achieved for the majority of relevant anatomic landmarks, which were viewed by the remote audience. Conclusion: The data presented in this study support the feasibility for mentoring and consultation to a remote audience with visual transmission of the surgical field, which is otherwise very difficult to share. Additionally, validation of technical protocols as teaching tools for robotic instrumentation and computer imaging of surgical fields was documented. With current technology advancements, surgery is now embracing computer technology, robotics, fiber optics, and telecommunications to provide telesurgery for mentoring, proctoring, and consultations. In this study, we report the successful integration of robotics, video teleconferencing, and intranet transmission for real-time distance education and consultation, while validating key elements in telesurgery technology.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.015 | 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".