Validation of Virtual Reality Simulation for Obstetric Ultrasonography
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: Ultrasonography is an important skill for obstetricians and gynecologists; however, trainees have highlighted ultrasonography as an area of deficiency in their training. We undertook a prospective cross-sectional comparative study to assess content and construct validity of an ultrasound virtual reality (VR) simulator (UltraSim). METHODS: Twenty-six physicians and sonographers of varied ultrasonography experience were recruited and divided into trainees (no formal ultrasonography training) and expert (certified) categories. They performed a VR simulation crown-rump length (CRL) ultrasound scan and growth ultrasound scan measuring biparietal diameter, occipitofrontal diameter, abdominal anteroposterior and transverse diameters, and femur length. Maximum pool depth (MPD), placental site, and fetal presentation were also assessed. Outcome measures included the mean absolute deviation and the variance of the absolute deviation from true measurements. Accuracy of determining placental site, fetal presentation, and MPD was assessed. The time taken to perform each type of scan was recorded. RESULTS: Trainees had significantly greater variation of measurement of CRL (P = 0.025) than the expert group. For late-pregnancy fetal biometry, the absolute deviation and the degree of variability for all measurements differed. These differences were statistically significant (P < 0.05) for all measurements except abdominal diameters and MPD. Trainees took significantly longer time to obtain CRL and fetal biometric scans (P < 0.001). All subjects correctly identified fetal presentation and placental site. CONCLUSIONS: Clinicians with differing ultrasonography expertise showed differing skill with the UltraSim VR simulator, demonstrating construct validity for skills needed in simulation. Consideration should be given to investigating whether trainees with minimal scanning experience can improve their clinical skills and efficiency with VR simulation.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it