Evaluation of clinical endobronchial ultrasound skills following clinical versus simulation training
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it