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 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 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".