Face, content and construct validity of a virtual reality simulator for robotic surgery (SEP Robot)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it