Fuzzy classification: towards evaluating performance on a surgical simulator.
Bibliographic record
Abstract
Computer-based surgical simulators such as the MIST-VR are able to provide scoring metrics such as time taken to complete a task, number of errors made, and economy of movement. Using MIST-VR's basic metrics, we explored the possibility of classifying skill levels using fuzzy logic. Our objective was to create a fuzzy classifier capable of classifying the performance of a subject training on a surgical simulator into 1 of 3 categories: Novice, Intermediate, and Expert. To accomplish this, we needed to establish a baseline skill level for each category. We had four laparoscopic surgeons, four surgical assistants/residents and four non-surgical staff/students with no laparoscopic experience perform two basic tasks on the simulator involving the placement of a ball into a box. We have found, through this preliminary study, that the results were inconclusive. We suspected a number of issues such as the size of our sample space used to train our classifier, and the difficulty of the chosen tasks adversely affected our results.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".