Fuzzy Set Theory for Performance Evaluation in a Surgical Simulator
Bibliographic record
Abstract
Increasing interest in computer-based surgical simulators as time- and cost-efficient training tools has introduced a new problem: objective evaluation of surgical performance based on scoring metrics provided by surgical simulators. This project employed fuzzy set theory to design a classifier for performance of a subject training on a surgical simulator, using three categories: novice, intermediate, and expert. The MIST-VR simulator was used in a user study of 26 subjects with three different surgical skill levels: 8 experienced laparoscopic surgeons (experts), 8 surgical assistants (intermediates), and 10 nurses (novices). Subjects were required to perform four trials of a suturing task and a knot-tying task on the simulator. The performance data were then used to train and test two fuzzy classifiers for each task. The fuzzy classifier was able to classify the users of the system. The models presented a highly nonlinear relationship between the inputs (performance metrics) and output (fuzzy score) of the system, which may not be effectively captured with classical classification approaches. Fuzzy classifiers, however, can offer effective tools to handle the complexity and fuzziness of objective evaluation of surgical performances.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".