The Gastrointestinal Endoscopy Competency Assessment Tool for Pediatric Colonoscopy
Bibliographic record
Abstract
OBJECTIVES: Validated assessment tools are required to support competency-based education. We aimed to assess the reliability and validity of the Gastrointestinal Endoscopy Competency Assessment Tool for Pediatric Colonoscopy (GiECATKIDS), an instrument developed by 41 North American experts using Delphi methodology. METHODS: GiECATKIDS consists of a 7-item global rating scale (GRS) and an 18-item checklist (CL). An attending physician assessed 104 colonoscopies performed at 3 North American hospitals by 56 endoscopists, including 25 novices (<50 previous procedures), 21 intermediates (50-250), and 10 advanced endoscopists (>500). Another observer rated procedures to assess interrater reliability using intraclass correlation coefficient (ICC). Test-retest reliability was measured with ICC comparing endoscopists' first and second procedure scores. Discriminative validity was examined by comparing experience level with scores. Concurrent validity was assessed by correlating scores with colonoscopy experience, cecal and terminal ileal intubation rates, and physician global assessment. RESULTS: Interrater reliability of the GiECATKIDS was high (total: ICC = 0.88; GRS: ICC = 0.79; CL: ICC = 0.89). Test-retest reliability was excellent (total: ICC = 0.94; GRS: ICC = 0.94; CL: ICC = 0.84). GiECATKIDS total, GRS, and CL scores differed significantly among novice, intermediate, and advanced endoscopists (P < 0.001). There was a significant positive correlation (P < 0.001) between scores and number of previous colonoscopies (total: ρ = 0.91, GRS: ρ = 0.92, CL: ρ = 0.84), cecal intubation rate (total: ρ = 0.82, GRS: ρ = 0.85, CL: ρ = 0.77), ileal intubation rate (total: ρ = 0.82, GRS: ρ = 0.82, CL: ρ = 0.80), and physician global assessment (total: ρ = 0.95, GRS: ρ = 0.94, CL: ρ = 0.89). CONCLUSIONS: The GiECATKIDS demonstrates strong reliability and validity as a measure of performance of pediatric colonoscopy that can be used to support training and assessment.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".