Retention of robot-assisted surgical skills in urological surgeons acquired using Mimic dV-Trainer
Bibliographic record
Abstract
INTRODUCTION: We assess the retention of robot-assisted surgical skills among urologic surgeons. METHODS: The robot-assisted surgery skills of 20 urologic surgeons were assessed using a Mimic dV-Trainer program (Mimic Technologies, Inc., Seattle, WA) consisting of 6 tasks. These 20 surgeons had no previous experience either using the Mimic dV-Trainer or acting as the main surgeon in robot-assisted surgery. The surgeons completed the program 4 times in a row; after 1 year, they completed it again for a fifth time. Performance scores were recorded using the Mimic dV-Trainer's built-in algorithm. RESULTS: For all 6 tasks, there were significant improvements to the scores in the fourth trials compared with those in the first trials. The scores in the fifth trials did not significantly decline compared with those in the fourth trials. There was no significant difference between the fifth trial scores of surgeons with laparoscopic surgery skills/experience and those without. CONCLUSION: Our results indicate that fundamental robot-assisted surgical skills can be retained in the long-term after they are acquired.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".