Transfer of skills on LapSim virtual reality laparoscopic simulator into the operating room in urology
Bibliographic record
Abstract
OBJECTIVE: Assessing the predictive validity of the LapSim simulator within a urology residency program. MATERIALS AND METHODS: Twelve urology residents at McGill University were enrolled in the study between June 2008 and December 2011. The residents had weekly training on the LapSim that consisted of 3 tasks (cutting, clip-applying, and lifting and grasping). They underwent monthly assessment of their LapSim performance using total time, tissue damage and path length among other parameters as surrogates for their economy of movement and respect for tissue. The last residents' LapSim performance was compared with their first performance of radical nephrectomy on anesthetized porcine models in their 4(th) year of training. Two independent urologic surgeons rated the resident performance on the porcine models, and kappa test with standardized weight function was used to assess for inter-observer bias. Nonparametric spearman correlation test was used to compare each rater's cumulative score with the cumulative score obtained on the porcine models in order to test the predictive validity of the LapSim simulator. RESULTS: The kappa results demonstrated acceptable agreement between the two observers among all domains of the rating scale of performance except for confidence of movement and efficiency. In addition, poor predictive validity of the LapSim simulator was demonstrated. CONCLUSIONS: Predictive validity was not demonstrated for the LapSim simulator in the context of a urology residency training program.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".