Endobronchial ultrasound learning curve in interventional pulmonary fellows
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: Little published data exist regarding the learning curve for endobronchial ultrasound-transbronchial needle aspiration (EBUS-TBNA). We sought to assess the improvement in skill as trainees learned EBUS-TBNA in a clinical setting. METHODS: This is a multicentre cohort study of EBUS-TBNA technical skill of interventional pulmonology (IP) fellows as assessed with EBUS-TBNA computer simulator testing every 25 clinical cases throughout IP fellowship training. RESULTS: Nine fellows from three academic centres in the United States and Canada were enrolled in the study. Ongoing improvements were seen for EBUS-TBNA efficiency score and percentage of lymph nodes correctly identified on ultrasound exam, even after 200 clinical cases. Expert-level technical skill was obtained for EBUS efficiency score and for percentage of lymph nodes correctly identified on ultrasound exam at a median of 212 and 164 procedures, respectively; however, 33% of fellows did not achieve expert-level technical skill for either metric during their fellowship training. Significant variation in learning curves of the fellows was observed. CONCLUSIONS: Significant variation is seen in the EBUS-TBNA learning curves of individual IP fellows and for individual procedure components, with ongoing improvement in EBUS-TBNA skill even after 200 clinical cases. These results highlight the need for validated, objective measures of individual competence, and can assist training programmes in ensuring adequate procedure volumes required for a majority of trainees to successfully complete these assessments.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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