Validation of an Endobronchial Ultrasound Simulator: Differentiating Operator Skill Level
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Endobronchial ultrasound (EBUS) is a revolutionary diagnostic procedure. There is currently no accepted method of assessing EBUS technical skill or competency. OBJECTIVES: This study aimed to validate a computer EBUS simulator in differentiating between operators of varying clinical EBUS experience. METHODS: A convenience sample (n = 22) of bronchoscopists was separated into four cohorts based on previous bronchoscopy experience: group A = novice bronchoscopists, no EBUS experience (n = 4), group B = expert bronchoscopists, no EBUS experience (n = 5), group C = basic clinical EBUS training (n = 9), group D = EBUS experts (n = 4). After a standardized introduction session on the EBUS simulator, participants performed 2 simulated cases on an EBUS simulator with performance metrics measured by the simulator. RESULTS: Significant differences between groups were noted for total procedure time, percentage of lymph nodes identified and percentage of successful biopsies (p < 0.05, ANOVA). Group D performed significantly better than all other groups for total procedure time and percentage of lymph nodes identified (p < 0.05). Group C performed significantly better than groups A and B for total procedure time, percentage of lymph nodes identified and percentage of successful biopsies (p < 0.05, ANOVA). CONCLUSIONS: An EBUS simulator can accurately discriminate between operators with different levels of clinical EBUS experience. EBUS simulators show promise as a tool for assessing training and evaluating competency.
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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