Validation of an Endobronchial Ultrasound Simulator: Differentiating Operator Skill Level
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.017 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".