Play Me Back: A Unified Training Platform for Robotic and Laparoscopic Surgery
Bibliographic record
Abstract
In this letter, we propose a training approach combining hand-over-hand and trial and error training approaches and we evaluate its effectiveness for both robotic and standard laparoscopic surgical training. The proposed approach makes use of the data of an expert collected while using the da Vinci Surgical System. We present our data collection system and how we use it in the proposed training approach. We conduct two user studies (N = 21 for each) to evaluate the effectiveness of this approach. Our results show that subjects trained using this combined approach can better balance the speed and accuracy of their task execution compared with others trained using only one of either hand-over-hand or trial and error training approaches. Moreover, this combined approach leads to the best performance when it comes to the transferability of the acquired skills when testing on another task. We show that the results of the two studies are consistent with an established model in the literature for motor skill learning. Moreover, our results show for the first time the feasibility of using a surgical robot and data collected from it as a training platform for conventional laparoscopic surgery without robotic assistance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".