Incorporation of the da Vinci Surgical Skills Simulator at urology Objective Structured Clinical Examinations (OSCEs): a pilot study.
Bibliographic record
Abstract
INTRODUCTION: To incorporate the da Vinci Surgical Skills Simulator (dVSSS) into Objective Structured Clinical Examinations (OSCEs) and to assess basic robotic skills of urology Post-Graduate Trainees (PGTs). MATERIALS AND METHODS: PGTs in post-graduate years (PGY-3 to PGY-5) from two Quebec urology training programs were recruited. During a 20 minute OSCE station, PGTs were asked to fill in a questionnaire and perform two tasks: pick and place, and energy dissection level 1. For each exercise, the norm-referenced method was used to establish a passing score to determine competency. The participant was considered competent in these two basic dVSSS exercises if he/she gained the passing score on both tasks. RESULTS: All nine PGTs who attended the OSCE voluntarily participated in the study. They had performed a median of 10 (IQR: 2.5-16) laparoscopic procedures, 2 (0-8) robotic procedures, and assisted 10 (IQR: 0-15) robotic procedures at the bedside prior to this OSCE. Based on a passing score of 90 for task 1 and 72 for task 2, there were 3 (33%) competent PGTs, all of whom were from PGY-5 level. Therefore, there was significant difference among PGY levels in terms of competency for the basic robotic skills tested (p = 0.01). When compared with PGTs, experts had performed significantly higher numbers of robotic procedures (5.2 +/- 2.4 versus 25 +/- 8.7; p = 0.02). However, there was no significant difference in the performance parameters between PGTs and experts in both tasks. CONCLUSION: This study confirms the feasibility of incorporating dVSSS into OSCEs to assess basic robotic skills of urology PGTs. Future studies need to include more complex exercises and larger sample size to expand on these results.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".