Virtual Reality Simulation in Teaching Fetoscopic Laser Placental Photocoagulation in Twin‐To‐Twin Transfusion Syndrome
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a novel virtual reality (VR) simulation system for training fetoscopic laser placental photocoagulation in twin-to-twin transfusion syndrome (TTTS). METHODS: A VR-based simulator incorporating Meta Quest headsets and custom-designed hardware was developed. The system features realistic anatomical modeling, integrated performance metrics, and progressive training modules. Validation involved 31 participants (11 experienced fetal therapy specialists, 10 fetal therapy fellows, and 10 other maternal-fetal medicine specialists) who evaluated the simulator across five domains using a standardized questionnaire. RESULTS: The simulator demonstrated excellent internal consistency (Cronbach's α = 0.92) with strong positive validation across all measured aspects. Training effectiveness received the highest endorsement (87%, 95% CI: 83%-91%), followed by user engagement (85%, 95% CI: 81%-89%). Experienced specialists rated environmental realism significantly higher (4.8 ± 0.3, p = 0.002), while fellows provided the strongest endorsement for training effectiveness (4.8 ± 0.3, p = 0.004). CONCLUSIONS: This VR simulator represents a significant advancement in TTTS surgical education, offering comprehensive training capabilities without requiring practice on actual patients. Initial testing demonstrates feasibility for both local and remote teaching applications, with potential advantages in cost, portability, and educational capabilities compared to traditional physical simulators.
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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 | 0.001 | 0.001 |
| 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".