Using Virtual-Reality Simulation to Assess Performance in Endobronchial Ultrasound
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Bibliographic record
Abstract
BACKGROUND: For optimal treatment of patients with non-small cell lung carcinoma, it is essential to have physicians with competence in endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). EBUS training and certification requirements are under discussion and the establishment of basic competence should be based on an objective assessment of performance. OBJECTIVES: The aims of this study were to design an evidence-based and credible EBUS certification based on a virtual-reality (VR) EBUS simulator test. METHODS: Twenty-two respiratory physicians were divided into 3 groups: experienced EBUS operators (group 1, n = 6), untrained novices (group 2, n = 8) and simulator-trained novices (group 3, n = 8). Each physician performed two standardized simulated EBUS-TBNA procedures. Simulator metrics with discriminatory ability were identified and reliability was explored. Finally, the contrasting-groups method was used to establish a pass/fail standard, and the consequences of this standard were explored. RESULTS: Successfully sampled lymph nodes and procedure time were the only simulator metrics that showed statistically significant differences of p = 0.047 and p = 0.002, respectively. The resulting quality score (QS, i.e. sampled lymph nodes per minute) showed an acceptable reliability and a generalizability coefficient of 0.67. Reliability of 0.8 could be obtained by testing in 4 procedures. Median QS was 0.24 (range 0.21-0.26) and 0.098 (range 0.04-0.21) for groups 1 and 2, respectively (p = 0.001). The resulting pass/fail standard was 0.19. Group 3 had a median posttraining QS of 0.11 (range 0-0.17). None of them met the pass/fail standard. CONCLUSIONS: With careful design of standardized tests, a credible standard setting and appropriate transfer studies, VR simulators could be an important first line in credentialing before proceeding to supervised performance 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 | 0.000 | 0.000 |
| 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