Recommendations for Quality Colonoscopy Reporting for Patients with Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: Consensus on what constitutes a quality colonoscopy report for patients with inflammatory bowel disease (IBD) is lacking. We developed a template for quality colonoscopy reporting that can be used broadly by endoscopists. METHODS: After a literature review of topics relevant to colonoscopy reporting, members of the Building Research in Inflammatory Bowel Disease Globally (BRIDGe) group and 2 external experts proposed candidate reporting elements. The RAND/University of California, Los Angeles appropriateness method was applied to rate the importance and feasibility of elements for inclusion in colonoscopy reports for patients with IBD. Panelists used the modified Delphi method to anonymously rate the importance and feasibility of candidate elements on a 1-to-9 scale (1-3: not important/feasible, 4-6: moderately important/feasible, 7-9: very important/feasible). Disagreement was assessed using a validated index. The panelists then met in person for discussion followed by a second round of voting. Elements rated a median of 7 or higher on importance after rerating were retained. RESULTS: One hundred two reporting elements were proposed. A total of 48 elements were retained across the four themes of "disease background," "findings and interventions," "Crohn's disease with an ileocolonic anastomosis," and "pouchoscopy." CONCLUSIONS: A comprehensive list of recommended elements for quality IBD colonoscopy reporting stratified by clinical scenario has been described, using a rigorous and evidence-based approach. These elements can be incorporated into endoscopy reporting software platforms. Standardized endoscopy reporting may improve the quality of care in IBD.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".