Bibliographic record
Abstract
Training simulators have proven their worth in a variety of fields, from piloting to air-traffic control to nuclear power station monitoring. Designing surgical simulators, however, poses the challenge of creating trainers that effectively instill not only high-level understanding of the steps to be taken in a given situation, but also the low-level "muscle-memory" needed to perform delicate surgical procedures. It is often impossible to build an ideal simulator that perfectly mimics the haptic experience of a surgical procedure, but by focussing on the aspects of the experience that are perceptually salient we can build simulators that effectively instill learning. We propose a general method for the design of surgical simulators that augment the perceptually salient aspects of an interaction. Using this method, we can increase skill-transfer rates without requiring expensive improvements in the capability of the rendering hardware or the computational complexity of the simulation. In this paper, we present our decomposition-based method for surgical simulator design, and describe a user-study comparing the training effectiveness of a haptic-search-task simulator designed using our method versus an unaugmented simulator. The results show that perception-based task decomposition can be used to improve the design of surgical simulators that effectively impart skill by targeting perceptually significant aspects of the interaction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".