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 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".