Validity evidence for endoscopic ultrasound competency assessment tools: Systematic review
Bibliographic record
Abstract
Abstract Background and study aims Competent endoscopic ultrasound (EUS) performance requires a combination of technical, cognitive, and non-technical skills. Direct observation assessment tools can be employed to enhance learning and ascertain clinical competence; however, there is a need to systematically evaluate validity evidence supporting their use. We aimed to evaluate the validity evidence of competency assessment tools for EUS and examine their educational utility. Methods We systematically searched five databases and gray literature for studies investigating EUS competency assessment tools from inception to May 2023. Data on validity evidence across five domains (content, response process, internal structure, relations to other variables, and consequences) were extracted and graded (maximum score 15). We evaluated educational utility using the Accreditation Council for Graduate Medical Education framework and methodological quality using the Medical Education Research Quality Instrument (MERSQI). Results From 2081 records, we identified five EUS assessment tools from 10 studies. All tools are formative assessments intended to guide learning, with four employed in clinical settings. Validity evidence scores ranged from 3 to 12. The EUS and ERCP Skills Assessment Tool (TEESAT), Global Assessment of Performance and Skills in EUS (GAPS-EUS), and the EUS Assessment Tool (EUSAT) had the strongest validity evidence with scores of 12, 10, and 10, respectively. Overall educational utility was high given ease of tool use. MERSQI scores ranged from 9.5 to 12 (maximum score 13.5). Conclusions The TEESAT, GAPS-EUS, and EUSAT demonstrate strong validity evidence for formative assessment of EUS and are easily implemented in educational settings to monitor progress and support learning.
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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 | 0.096 | 0.474 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.028 | 0.021 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".