Robotic surgery basic skills training: Evaluation of a pilot multidisciplinary simulation-based curriculum
Bibliographic record
Abstract
PURPOSE: Simulation-based training improves clinical skills, while minimizing the impact of the educational process on patient care. We present results of a pilot multidisciplinary, simulation-based robotic surgery basic skills training curriculum (BSTC) for robotic novices. METHODS: A 4-week, simulation-based, robotic surgery BSTC was offered to the Departments of Surgery and Obstetrics & Gynecology (ObGyn) at the University of Toronto. The course consisted of various instructional strategies: didactic lecture, self-directed online-training modules, introductory hands-on training with the da Vinci robot (dVR) (Intuitive Surgical Inc., Sunnyvale, CA), and dedicated training on the da Vinci Skills Simulator (Intuitive Surgical Inc., Sunnyvale, CA) (dVSS). A third of trainees participated in competency-based dVSS training, all others engaged in traditional time-based training. Pre- and post-course skill testing was conducted on the dVR using 2 standardized skill tasks: ring transfer (RT) and needle passing (NP). Retention of skills was assessed at 5 months post-BSTC. RESULTS: A total of 37 participants completed training. The mean task completion time and number of errors improved significantly post-course on both RT (180.6 vs. 107.4 sec, p < 0.01 and 3.5 vs. 1.3 sec, p < 0.01, respectively) and NP (197.1 vs. 154.1 sec, p < 0.01 and 4.5 vs. 1.8 sec, p = 0.04, respectively) tasks. No significant difference in performance was seen between specialties. Competency-based training was associated with significantly better post-course performance. The dVSS demonstrated excellent face validity. CONCLUSIONS: The implementation of a pilot multidisciplinary, simulation-based robotic surgery BSTC revealed significantly improved basic robotic skills among novice trainees, regardless of specialty or level of training. Competency-based training was associated with significantly better acquisition of basic robotic skills.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".