Evaluation of a Novel Method of Teaching Endobronchial Ultrasound: Physician‐ Versus Respiratory Therapist‐Proctored Simulation Training
Bibliographic record
Abstract
BACKGROUND: Computer endobronchial ultrasound (EBUS) simulators have been demonstrated to improve trainee procedural skills before attempting to perform EBUS procedures on patients. OBJECTIVE: To compare EBUS performance following training with computer simulation proctored by EBUS-trained respiratory therapists versus the same simulation training proctored by an interventional respirologist. METHODS: The present analysis was a prospective study of respiratory medicine trainees learning EBUS. Two cohorts of trainees were evaluated using a previously validated method using simulated cases with performance metrics measured by the simulator. Group 1 underwent EBUS training by performing 15 procedures on an EBUS simulator (n=4) proctored by an interventional respirologist. Group 2 received identical training proctored by a respiratory therapist with special training in EBUS (n=10). RESULTS: No significant differences between group 1 and group 2 were apparent for the primary outcome measures of total procedure time (15.15±1.34 min versus 14.78±2.88 min; P=0.816), the percentage of lymph nodes successfully identified (88.8±5.4 versus 80.91±8.9; P=0.092) or the percentage of successful biopsies (100.0±0.0 versus 98.75±3.95; P=0.549). The learning curves were similar between groups, and did not show an obvious plateau after 19 simulated procedures in either group. DISCUSSION: Acquisition of basic EBUS technical skills can be achieved using computer EBUS simulation proctored by specially trained respiratory therapists or by an interventional respirologist. There appeared to be no significant advantage to having an interventional respirologist proctor the computer EBUS simulation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".