A Surgical Virtual Reality Simulator Distinguishes Between Expert Gynecologic Laparoscopic Surgeons and Perinatologists
Bibliographic record
Abstract
BACKGROUND: Concern regarding the quality of surgical training in obstetrics and gynecology residency programs is focusing attention on competency based education. Because open surgical skills cannot necessarily be translated into laparoscopic skills and with minimally invasive surgery becoming standard in operative gynecology, the discrepancy in training between obstetrics and gynecology will widen. Training on surgical simulators with virtual reality may improve surgical skills. However, before incorporation into training programs for gynecology residents the validity of such instruments needs to first be established. We sought to prove the construct validity of a virtual reality laparoscopic simulator, the SurgicalSim(TM), by showing its ability to distinguish between surgeons with different laparoscopic experience. METHODS: Eleven gynecologic surgeons (experts) and 11 perinatologists (controls) completed 3 tasks on the simulator, and 10 performance parameters were compared. RESULTS: The experts performed faster, more efficiently, and with fewer errors, proving the construct validity of the SurgicalSim. CONCLUSIONS: Laparoscopic virtual reality simulators can measure relevant surgical skills and so distinguish between subjects having different skill levels. Hence, these simulators could be integrated into gynecology resident endoscopic training and utilized for objective assessment. Second, the skills required for competency in obstetrics cannot necessarily be utilized for better performance in laparoscopic gynecology.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".