Role of extensible physics engine in surgery simulations
Bibliographic record
Abstract
Surgery simulations are of significant value in medical training as they provide a less costly mean of training new surgeons. The quality of a surgery simulation is defined by how realistic it is in a physical sense. These physics aspects however have little to do the high-level functionalities of the application. Most VR (virtual reality) applications use libraries called physics engine for enforcing physics laws in their virtual worlds. Although the concept of using physics engines is ideal for development of VR applications, in practice it does impose many limitations. For instance, there are many physical laws in the world, and one single physics engine cannot provide all of them. In addition, each physical law can be implemented differently; some are optimized for fast response, others are designed for maximum precision. Most existing physics engines are specialized for common applications such as gaming, robotic or flight simulations. In surgery applications, the requirements are different; therefore different physics laws are needed. In particular, laws are needed for simulating cutting and skin deformation. In addition, these laws must be enforced with a high precision level. We discuss how xPheve was used to develop a cataract eye surgery simulation. xPheve is a physics engine that allows extension and customization of the simulation physics through development and integration of reusable law components. This work discusses how xPheve can benefit the development of the surgery simulations.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".