Baseline Laparoscopic Skill May Predict Baseline Robotic Skill and Early Robotic Surgery Learning Curve
Bibliographic record
Abstract
INTRODUCTION: Robotic surgery is associated with a learning curve unique to each trainee. Knowledge about a trainee's baseline skill and learning curve would facilitate the development of a more individualized training curriculum. The aim of our study was to determine whether baseline laparoscopic skill is predictive of one's baseline robotic skill and short-term learning curve. METHODS: Trainees from four different surgical specialties were included in the study. Each trainee participated in a 4-week, simulation-based robotic surgery basic skills training course. Precourse, baseline laparoscopic and robotic skills were assessed using validated test tasks; a basic peg transfer (PT) and an advanced intracorporeal suturing and knot tying (ISKT) task. Trainee robotic skill was assessed again 1 week postcourse. Each task performance was video recorded and scored by two blinded expert surgeons. RESULTS: A total of 32 trainees were included; 14 urology, 7 gynecology, 8 thoracic Sx, 3 general Sx. Most (91%) were senior residents or clinical fellows and 50% had no prior robotic experience. There were no differences in baseline laparoscopic and robotic skill related to reported prior robotic experience. Between specialties, no differences were seen on baseline laparoscopic skill and only small differences were seen on baseline robotic skill. Both baseline Lap PT (p = 0.01) and Lap ISKT (p = 0.01) performances correlated with baseline robotic ISKT performance, but not robotic PT scores. Only baseline Lap ISKT performance correlated with postcourse robotic PT (p = 0.01) and ISKT (p < 0.01) performance. Baseline robotic ISKT scores, but not PT scores, correlated with postcourse robotic performance (p = 0.02 for PT, p < 0.01 for ISKT). CONCLUSIONS: In this study, a trainee's baseline laparoscopic skill correlated with certain baseline robotic skills. Better baseline performance on an advanced, but not basic, laparoscopic and robotic skill task may correlate with a shorter learning curve for basic robotic skills. Further exploration of this finding may yield better training curricula.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".