Modified Delphi Process for the Development of Choosing Wisely for Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: The prevalence and incidence of inflammatory bowel disease (IBD) in North America is among the highest in the world and imparts substantial direct and indirect medical costs. The Choosing Wisely Campaign was launched in wide variety of medical specialties and disciplines to reduce unnecessary or harmful tests or treatment interventions. METHODS: The Choosing Wisely list for IBD was developed by the Canadian IBD Network for Research and Growth in Quality Improvement (CINERGI) in collaboration with Crohn's and Colitis Canada (CCC) and the Canadian Association of Gastroenterology (CAG). Using a modified Delphi process, 5 recommendations were selected from an initial list of 30 statements at a face-to-face consensus meeting. RESULTS: The 5 things physicians and patients should question: (1) Don't use steroids (e.g., prednisone) for maintenance therapy in IBD; (2) Don't use opioids long-term to manage abdominal pain in inflammatory bowel disease (IBD); (3) Don't unnecessarily prolong the course of intravenous corticosteroids in patients with acute severe ulcerative colitis (UC) in the absence of clinical response; (4) Don't initiate or escalate long-term medical therapies for the treatment of IBD based only on symptoms; and (5) Don't use abdominal computed tomography (CT) scan to assess IBD in the acute setting unless there is suspicion of a complication (obstruction, perforation, abscess) or a non-IBD etiology for abdominal symptoms. CONCLUSIONS: The Choosing Wisely recommendations will foster patient-physician discussions to optimize IBD therapy, reduce adverse effects from testing and treatment, and reduce medical expenditure.
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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 | 0.268 | 0.321 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".