Assessment and learning curve evaluation of endobronchial ultrasound skills following simulation and clinical training
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Endobronchial ultrasound is a revolutionary diagnostic pulmonary procedure. The use of a computer endobronchial ultrasound simulator could improve trainee procedural skills before attempting to perform procedures on patients. This study aims to compare endobronchial ultrasound performance following training with simulation versus conventional training using patients. METHODS: A prospective study of pulmonary medicine and thoracic surgery trainees. Two cohorts of trainees were evaluated using simulated cases with performance metrics measured by the simulator. Group 1 received endobronchial ultrasound training by performing 15 cases on an endobronchial ultrasound simulator (n=4). Group 2 received endobronchial ultrasound training by doing 15-25 cases on patients (n=9). RESULTS: Total procedure time was significantly shorter in group 1 than group 2 (15.15 (±1.34) vs 20.00 (±3.25) min, P<0.05). The percentage of lymph nodes successfully identified was significantly better in group 1 than group 2 (89.8 (±5.4) vs 68.1 (±5.2), P < 0.05). There was no difference between group 1 and group 2 in the percentage of successful biopsies (100.0 (±0.0) vs 90.4 (±11.5), P=0.13). The learning curves for simulation trained fellows did not show an obvious plateau after 19 simulated cases. CONCLUSIONS: Using an endobronchial ultrasound simulator leads to more rapid acquisition of skill in endobronchial ultrasound compared with conventional training methods, as assessed by an endobronchial ultrasound simulator. Endobronchial ultrasound simulators show promise for training with the advantage of minimizing the burden of procedural learning on patients.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".