Construct validity of the LapSim virtual reality laparoscopic
Bibliographic record
Abstract
Objective: We assessed the construct validity of the LapSim laparoscopic surgical simulator in a urology residency training program.Methods: In total, 15 residents participated in the study betweenJuly 2007 and July 2008. The subjects were tested six times at onemonth intervals on three skill tasks (lifting and grasping, cuttingand clip application) using the LapSim laparoscopic simulator. Thetesting sessions were divided into the first three sessions (seminar1), and the subsequent three sessions (seminar 2). We evaluated thefollowing parameters: total time, path length, angular path length,tissue damage, maximum damage and stretch damage. The subjectswere divided into junior (PGY 1,2) and senior resident groups(PGY 3,4,5). The Wilcoxon Signed-Rank test for paired sampleswas used to compare the performances of the juniors and seniorsduring seminar 1 to their performance in seminar 2 to determinewhether there was improvement over time. The Wilcoxon Rank-Sum test for independent samples was used to compare the performance of the juniors to that of the seniors for seminar 1, seminar 2 and the combination of both seminars to determine whether the more experienced senior residents performed better than the lessexperienced juniors.Results: No significant performance improvement between testingsessions could be demonstrated. Similarly, there was no significantdifference in performance between junior and senior residents.Conclusions: Construct validity could not be demonstrated for thetotal time, path length, angular path length and tissue handlingparameters of the LapSim laparoscopic surgical simulator whenexamined within the context of a urology residency program.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".