Identifying and Classifying Problem Areas in Laparoscopic Skills Acquisition: Can Simulators Help?
Bibliographic record
Abstract
BACKGROUND: Independent learning with simulators might be improved if simulators could "diagnose the learner" by identifying common novice problems, thereby directing self-guided learning. Our goal was to determine if data collected by a virtual reality simulator could be used to predict the problem areas in novice trainees' laparoscopic performance. METHOD: Fourteen expert laparoscopists were interviewed to identify common problem areas experienced by novices as they learn laparoscopy. Two expert laparoscopists rated 20 novices' simulator performances regarding the extent of each problem area. RESULTS: Moderate interrater reliability and high "interproblem" correlations suggest that experts did not reliably distinguish between the five identified problem areas as expected. CONCLUSIONS: The process by which expert teachers "diagnose" student difficulties did not reduce well to numeric assessments using linear independent scales in the simulated context. This finding raises challenges for our ability to identify such difficulties using the data collected by simulators.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".