Play Me Back: A Unified Training Platform for Robotic and Laparoscopic Surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it