Wet Laboratory Versus Computer Simulation for Learning Endobronchial Ultrasound: A Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Linear endobronchial ultrasound with transbronchial needle aspiration (EBUS-TBNA) is a revolutionary bronchoscopic procedure that is challenging to learn. OBJECTIVES: To compare two methods used to teach EBUS-TBNA: wet laboratory (lab) versus computer EBUS-TBNA simulation. METHODS: A prospective, randomized study of respirologists, thoracic surgeons and trainees learning EBUS-TBNA at a two-day continuing medical education course. All subjects received education via a series of lectures and live cases, followed by randomization to learn EBUS-TBNA predominantly either by wet lab simulation (n=6) or computer simulation (n=6). All subjects then completed testing of their EBUS-TBNA skills via a previously validated method using simulated cases on EBUS-TBNA simulators and questionnaires evaluating learner preferences. RESULTS: There were no significant differences between the computer EBUS-TBNA simulator group and the wet lab group in procedure time (25.3±6.1 min versus 25.2±2.5 min; P=0.984) and percentage of successful biopsies (81.3±14.9% versus 74.0±17.3%; P=0.453). The computer simulator group performed significantly better than the wet lab group in the percentage of lymph nodes correctly identified (70.4±16.7% versus 42.9±19.9%; P=0.002). Wet lab simulation was associated with increased learner confidence with operating the real EBUS-TBNA bronchoscope. All subjects responded that wet lab and computer EBUS-TBNA simulation offered important complementary learning opportunities. CONCLUSION: Computer EBUS-TBNA simulation and wet lab simulation are effective methods of learning basic EBUS-TBNA skills and appeared to be complementary.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".