Development of the Gastrointestinal Endoscopy Competency Assessment Tool for Pediatric Colonoscopy (GiECAT<sub>KIDS</sub>)
Bibliographic record
Abstract
OBJECTIVES: Many aspects of pediatric colonoscopy differ from adult practice. To date, there is no validated measure of endoscopic competence for use in pediatrics. Using Delphi methodology, we aimed to determine expert consensus regarding items required on a checklist and global rating scale designed to assess the competence of clinicians performing colonoscopy on pediatric patients. METHODS: A total of 41 North American pediatric endoscopy experts rated potential checklist and global rating items for their importance as indicators of the competence of trainees learning to perform pediatric colonoscopy. Responses were analyzed and re-sent to the panel for further ratings until consensus was reached. Items that ≥ 80% of experts rated as ≥ 4 out of 5 were included in the final instrument. Consensus items were compared with those items deemed by adult endoscopy experts as fundamental to assessing the performance of adult colonoscopy. RESULTS: Five rounds of surveys were completed with response rates ranging from 76% to 100%. Seventy-five checklist and 38 global rating items were reduced to 18 checklist and 7 global rating items that reached consensus. Three pediatric checklist items differed from those considered to be critical adult indicators, whereas 4 items on the latter did not reach consensus among pediatric experts. CONCLUSIONS: Delphi methodology allowed for achievement of expert consensus regarding essential items to be included in the Gastrointestinal Endoscopy Competency Assessment Tool for Pediatric Colonoscopy (GiECATKIDS), a measure of endoscopic competence specific to performing pediatric colonoscopy. Key differences in the checklist items, compared with items reaching consensus during a separate adult Delphi process using the same indicators, emphasize the need for a pediatric-specific tool.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".