A97 TOOLS FOR DIRECT OBSERVATION AND ASSESSMENT OF COLONOSCOPY: A SYSTEMATIC REVIEW OF VALIDITY EVIDENCE
Bibliographic record
Abstract
Abstract Background An increasing focus on quality and safety in colonoscopy has led to broader implementation of competency-based educational systems that enable documentation of trainees’ achievement of the knowledge, skills, and attitudes needed for independent practice. The meaningful assessment of competence in colonoscopy is critical to this process. While there are many published tools that assess competence in performing colonoscopy, there is a wide range of underlying validity evidence. Tools with strong evidence of validity are required to support feedback provision, optimize learner capabilities, and document competence. Aims We aimed to evaluate the strength of validity evidence that supports available colonoscopy direct observation assessment tools using the unified framework of validity. Methods We systematically searched five databases for studies investigating colonoscopy direct observation assessment tools from inception until April 8, 2020. We extracted data outlining validity evidence from the five sources (content, response process, internal structure, relations to other variables, and consequences) and graded the degree of evidence, with a maximum score of 15. We assessed educational utility using an Accreditation Council for Graduate Medical Education framework and methodological quality using the Medical Education Research Quality Instrument (MERSQI). Results From 10,841 records, we identified 27 studies representing 13 assessment tools (10 adult, 2 pediatric, 1 both). All tools assessed technical skills, while 10 assessed cognitive and integrative skills. Validity evidence scores ranged from 1–15. The Assessment of Competency in Endoscopy (ACE) tool, the Direct Observation of Procedural Skills (DOPS) tool, and the Gastrointestinal Endoscopy Competency Assessment Tool (GiECAT) had the strongest validity evidence, with scores of 13, 15, and 14, respectively. Most tools were easy to use and interpret and required minimal resources. MERSQI scores ranged from 9.5–11.5 (maximum score 14.5). Conclusions The ACE, DOPS, and GiECAT have strong validity evidence compared to other assessments. Future studies should identify barriers to widespread implementation and report on use of these tools in credentialing purposes. Funding Agencies None
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.
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 | 0.065 | 0.284 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.031 | 0.022 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".