Assessment and learning curve evaluation of endobronchial ultrasound skills following simulation and clinical 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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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 | 0.000 |
| 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