When Trainees Reach Competency in Performing EUS: A Systematic Review
Bibliographic record
Abstract
Introduction: National guidelines currently advocate for the completion of 150 endoscopic ultrasound (EUS) procedures prior to assessing trainee competency. However, these recommendations were founded on expert opinion and limited evidence. In the era of competency-based medical education new evidence has emerged suggesting that this historical procedural threshold is underestimating training requirements. Therefore, we systematically evaluated the learning curve for achieving competency in EUS. Methods: Two authors independently searched MEDLINE from 1946 to March 25, 2016 as well as the grey literature for full-text citations assessing the learning curve for EUS competency. A learning curve was defined as either a tabulated or graphic depiction of competency as a function of rising EUS experience with a minimum of two data points along the learning curve being required. Results: In total 8 studies warranted inclusion, which assessed 28 trainees and 7051 EUS procedures. Upon study stratification: 3 studies assessed mucosal lesion evaluation, 3 studies assessed EUS fine needle aspiration (EUS-FNA) and 2 studies assessed comprehensive competency. Concerning the 3 studies, which assessed mucosal lesion T-staging accuracy, competency was achieved by 65 to 231 EUS procedures. Concerning the 3 studies, which assessed EUS-FNA, competency was achieved by 30 to 40 EUS procedures. Of the 2 studies, which assessed comprehensive competency, only 4 of 17 trainees achieved competency by 225 to 295 EUS procedures. Conclusion: As competency assessment in EUS has matured to now use comprehensive competency, which more closely reflects clinical practice, the number of EUS procedures required to obtain competency has surpassed current recommendations. Therefore, specialty societies and advanced endoscopy training programs need to revisit the current structure of EUS training.
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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 | 0.018 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".