Role of extensible physics engine in surgery simulations
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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