Setting priorities for comparative effectiveness research in inflammatory bowel disease: Results of an international provider survey, expert rand panel, and patient focus groups
Bibliographic record
Abstract
BACKGROUND: Comparative effectiveness research (CER) is an emerging field that compares the relative effectiveness of alternative strategies to prevent, diagnose, or treat patients who are typical of day-to-day practice. We developed a priority list of CER topics for inflammatory bowel disease (IBD). METHODS: Following the Institute of Medicine's approach, we developed and administered a survey to gastroenterologists asking for important CER topics in IBD. Two patient focus groups were convened to solicit additional CER studies. CER topics were presented to the expert panel using the RAND/UCLA methodology. Following initial ratings, the panel met to discuss and re-rate priorities. The top 10 CER topics were identified using a point-allocation system. RESULTS: Responses were collated into 234 CER topics across 21 categories, of which 87 were prioritized for discussion and re-rated. Disagreement regarding priorities was observed in 5 of 87 studies. We utilized a point-allocation system to prioritize the top-10 CER topics. These related to comparing the effectiveness of: biomarkers in IBD; withdrawal of anti-tumor necrosis factor (TNF) or immunomodulators for Crohn's disease in remission; mucosal healing as an endpoint of treatment; infliximab levels versus standard infliximab dosing; anti-TNF monotherapy versus combination therapy in patients failing thiopurines; safety of long-term treatment options; anti-TNF versus thiopurines for prevention of postoperative recurrence; and treatment options for steroid-refractory UC. CONCLUSIONS: We systematically developed a list of high-priority IBD topics for CER based on a survey of gastroenterologists, expert review, and patient input. This list may guide IBD research toward the most important CER studies.
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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 | 0.640 | 0.659 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".