Validity evidence for observational ERCP competency assessment tools: a systematic review
Bibliographic record
Abstract
BACKGROUND : Assessment of competence in endoscopic retrograde cholangiopancreatography (ERCP) is critical for supporting learning and documenting attainment of skill. Validity evidence supporting ERCP observational assessment tools has not been systematically evaluated. METHODS : We conducted a systematic search using electronic databases and hand-searching from inception until August 2021 for studies evaluating observational assessment tools of ERCP performance. We used a unified validity framework to characterize validity evidence from five sources: content, response process, internal structure, relations to other variables, and consequences. Each domain was assigned a score of 0-3 (maximum score 15). We assessed educational utility and methodological quality using the Accreditation Council for Graduate Medical Education framework and the Medical Education Research Quality Instrument, respectively. RESULTS : From 2769 records, we included 17 studies evaluating 7 assessment tools. Five tools were studied for clinical ERCP, one for simulated ERCP, and one for simulated and clinical ERCP. Validity evidence scores ranged from 2 to 12. The Bethesda ERCP Skills Assessment Tool (BESAT), ERCP Direct Observation of Procedural Skills Tool (ERCP DOPS), and The Endoscopic Ultrasound (EUS) and ERCP Skills Assessment Tool (TEESAT) had the strongest validity evidence, with scores of 10, 12, and 11, respectively. Regarding educational utility, most tools were easy to use and interpret, and required minimal additional resources. Overall methodological quality (maximum score 13.5) was strong, with scores ranging from 10 to 12.5. CONCLUSIONS : The BESAT, ERCP DOPS, and TEESAT had strong validity evidence compared with other assessments. Integrating tools into training may help drive learners' development and support competency decision making.
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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 | 0.133 | 0.545 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".