General algorithms for laparoscopic surgical simulators
Why this work is in the frame
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Bibliographic record
Abstract
Recent advances in fields such as modeling of deformable objects, haptic technologies, immersive technologies, computation capacity and virtual environments have created the conditions to offer novel and suitable training tools and learning methods in the medical area. One of these training tools is the virtual surgical simulator, which has no limitations of time or risk, unlike conventional methods of training. Moreover, these simulators allow for the quantitative evaluation of the surgeon performance, giving the possibility to create performance standards in order to define if the surgeon is well prepared to execute a determined surgical procedure on a real patient. This paper describes the development of a virtual simulator for laparoscopic surgery. The simulator allows the multimodal interaction between the surgeon and the surgical virtual environment using visual and haptic feedback devices. To make the experience of the surgeon closer to the real surgical environment a specific user interface was developed. Additionally in this paper we describe some implementations carried out to face typical challenges presented in surgical simulators related to the tradeoff between real-time performance and high realism; for instance, the deformation of soft tissues are simulated using a GPU (Graphics Processor Unit) -based implementation of the mass-spring model. In this case, we explain the algorithms developed taking into account the particular case of a cholecystectomy procedure in laparoscopic surgery.
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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 | 0.001 | 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