Simulator training for endobronchial ultrasound: a randomised controlled trial
Bibliographic record
Abstract
Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is very operator dependent and has a long learning curve. Simulation-based training might shorten the learning curve, and an assessment tool with solid validity evidence could ensure basic competency before unsupervised performance.A total of 16 respiratory physicians, without EBUS experience, were randomised to either virtual-reality simulator training or traditional apprenticeship training on patients, and then each physician performed EBUS-TBNA procedures on three patients. Three blinded, independent assessor assessed the video recordings of the procedures using a newly developed EBUS assessment tool (EBUSAT).The internal consistency was high (Cronbach's α=0.95); the generalisability coefficient was good (0.86), and the tool had discriminatory ability (p<0.001). Procedures performed by simulator-trained novices were rated higher than procedures performed by apprenticeship-trained novices: mean±sd are 24.2±7.9 points and 20.2±9.4 points, respectively; p=0.006. A pass/fail standard of 28.9 points was established using the contrasting groups method, resulting in 16 (67%) and 20 (83%) procedures performed by simulator-trained novices and apprenticeship-trained novices failing the test, respectively; p<0.001.The endobronchial ultrasound assessment tool could be used to provide reliable and valid assessment of competence in EBUS-TBNA, and act as an aid in certification. Virtual-reality simulator training was shown to be more effective than traditional apprenticeship training.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".