When trainees reach competency in performing endoscopic ultrasound: a systematic review
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
Abstract Background/Study aim The American Society for Gastrointestinal Endoscopy (ASGE) recommends that trainees complete 150 endoscopic ultrasound (EUS) procedures before assessing competency. However, this recommendation is largely based on limited evidence and expert opinion. With new evidence suggesting that this historical threshold is underestimating training requirements, we evaluated the learning curve for achieving competency in EUS. Patients/Materials and methods Two investigators independently searched MEDLINE for full-text citations assessing the learning curve for achieving competency in EUS in the period 1946 to 25 March 2016. A learning curve was defined as either a tabulated or graphic representation of competency as a function of increasing EUS experience. Results Eight studies assessing 28 trainees and 7051 EUS procedures were included. When stratifying studies based on procedural indication: three studies assessed competency in evaluating mucosal lesions, three studies assessed competency in EUS fine-needle aspiration (EUS-FNA), and two studies assessed comprehensive competency. Among studies assessing mucosal lesion T-staging accuracy, competency was achieved by 65 to 231 procedures. Among studies assessing EUS-FNA, competency was achieved by 30 to 40 procedures. Among the two studies assessing comprehensive competency in EUS, competency was not achieved in either study across all trainees. Only four of 17 trainees reached competency by 225 to 295 EUS procedures. Conclusion As EUS competency assessment has evolved to more closely reflect independent clinical practice, the number of procedures required to achieve competency has risen well above ASGE recommendations. Advanced endoscopy training programs and specialty societies need to re-assess the structure of EUS training.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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