Face, content and construct validity of a virtual reality simulator for robotic surgery (SEP Robot)
Bibliographic record
Abstract
INTRODUCTION: This study aims to establish face, content and construct validation of the SEP Robot (SimSurgery, Oslo, Norway) in order to determine its value as a training tool. SUBJECTS AND METHODS: The tasks used in the validation of this simulator were arrow manipulation and performing a surgeon's knot. Thirty participants (18 novices, 12 experts) completed the procedures. RESULTS: The simulator was able to differentiate between experts and novices in several respects. The novice group required more time to complete the tasks than the expert group, especially suturing. During the surgeon's knot exercise, experts significantly outperformed novices in maximum tightening stretch, instruments dropped, maximum winding stretch and tool collisions in addition to total task time. A trend was found towards the use of less force by the more experienced participants. CONCLUSIONS: The SEP robotic simulator has demonstrated face, content and construct validity as a virtual reality simulator for robotic surgery. With steady increase in adoption of robotic surgery world-wide, this simulator may prove to be a valuable adjunct to clinical mentorship.
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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 | 0.004 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".