Virtual-reality simulation to assess performance in hip fracture surgery
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Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Internal fixation of hip fractures is a common and important procedure that orthopedic surgeons must master early in their career. Virtual-reality training could improve initial skills, and a simulation-based test would make it possible to ensure basic competency of junior surgeons before they proceed to supervised practice on patients. The aim of this study was to develop a reliable and valid test with credible pass/fail standards. METHODS: 20 physicians (10 untrained novices and 10 experienced orthopedic surgeons) each performed 3 internal fixation procedures of an undisplaced femoral neck fracture: 2 hook-pins, 2 screws, and a sliding hip screw. All procedures were preformed on a trauma simulator. Performance scores for each procedure were obtained from the predefined metrics of the simulator. The inter-case reliability of the simulator metrics was explored by calculation of intra-class correlation coefficient. Validity was explored by comparison between novices' and experts' scores using independent-samples t-test. A pass/fail standard was set by the contrasting-groups method and the consequences were explored. RESULTS: The percentage of maximum combined score (PM score) showed an inter-case reliability of 0.83 (95% CI: 0.65-0.93) between the 3 procedures. The mean PM score was 30% (CI: 7-53) for the novices and 76% (CI: 68-83) for the experienced surgeons. The pass/fail standard was set at 58%, resulting in none of the novices passing the test and a single experienced surgeon failing the test. INTERPRETATION: The simulation-based test was reliable and valid in our setting, and the pass/fail standard could discriminate between novices and experienced surgeons. Potentially, training and testing of future junior surgeons on a virtual-reality simulator could ensure basic competency before proceeding to supervised practice on patients.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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