Evaluation of clinical endobronchial ultrasound skills following clinical versus simulation training
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Endobronchial ultrasound with transbronchial needle aspiration (EBUS-TBNA) is a pulmonary procedure that can be challenging to learn. This study aims to compare trainee EBUS-TBNA performance during clinical procedures, following training with a computer EBUS-TBNA simulator versus conventional clinical EBUS-TBNA training. METHODS: A prospective study of pulmonary trainees performing EBUS-TBNA procedures on patients with suspected lung cancer and mediastinal adenopathy. Two cohorts of trainees were each evaluated while performing EBUS-TBNA on two patients. Group 1 received training by performing 15 cases on an EBUS-TBNA simulator (n = 4) and had never performed a clinical EBUS-TBNA procedure. Group 2 received training by doing 15-25 EBUS-TBNA procedures on patients (n = 4). RESULTS: There was no significant difference in the primary outcome measure of total EBUS-TBNA procedure time/number of successful aspirates between Groups 1 and 2 (3.95 (±0.93) vs 3.64 (±0.89), P = 0.51). Total learner EBUS-TBNA procedure time in minutes (23.67 (±5.58) vs 21.81 (±5.36), P = 0.17) and percentage of successful aspirates (93.3% (±5.8%) vs 86.3% (±6.7%), P = 0.12) were not significantly different between Group 1 and Group 2. The only significant difference found between Group 1 and Group 2 was time to intubation in minutes (0.99 (±0.46) vs 0.50 (±0.42), P = 0.04). CONCLUSIONS: EBUS-TBNA simulator use leads to rapid acquisition of clinical EBUS-TBNA skills comparable with that obtained with conventional training methods using practice on patients, suggesting that skills learned using an EBUS-TBNA simulator are transferable to clinical EBUS-TBNA performance. EBUS-TBNA simulators show promise for training, potentially 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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".