Getting started with robotics in general surgery with cholecystectomy: the Canadian experience.
Bibliographic record
Abstract
BACKGROUND: The value of robotics in general surgery may be for advanced minimally invasive procedures. Unlike other specialties, formal fellowship training opportunities for robotic general surgery are few. As a result, most surgeons currently develop robotic skills in practice. Our goal was to determine whether robotic cholecystectomy is a safe and effective bridge to advanced robotics in general surgery. METHODS: Before performing advanced robotic procedures, 2 surgeons completed the Intuitive Surgical da Vinci training course and agreed to work together on all procedures. Clinical surgery began with da Vinci cholecystectomy with a plan to begin advanced procedures after at least 10 cholecystectomies. We performed a retrospective review of our pilot series of robotic cholecystectomies and compared them with contemporaneous laparoscopic controls. The primary outcome was safety, and the secondary outcome was learning curve. RESULTS: There were 16 procedures in the robotics arm and 20 in the laparoscopic arm. Two complications (da Vinci port-site hernia, transient elevation of liver enzymes) occurred in the robotic arm, whereas only 1 laparoscopic patient (slow to awaken from anesthetic) experienced a complication. None was significant. The mean time required to perform robotic cholecystectomy was significantly longer than laparoscopic surgery (91 v. 41 min, p < 0.001). The mean time to clear the operating room was significantly longer for robotic procedures (14 v. 11 min, p = 0.015). We observed a trend showing longer mean anesthesia time for robotic procedures (23 v. 15 min). Regarding learning curve, the mean operative time needed for the first 3 robotic procedures was longer than for the last 3 (101 v. 80 min); however, this difference was not significant. Since this experience, the team has confidently gone on to perform robotic biliary, pancreatic, gastresophageal, intestinal and colorectal operations. CONCLUSION: Robotic cholecystectomy can be performed reliably; however, owing to the significant increase in operating room resources, it cannot be justified for routine use. Our experience, however, demonstrates that robotic cholecystectomy is one means by which general surgeons may gain confidence in performing advanced robotic procedures.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".