Interpreting Registrational Clinical Trials of Biological Therapies in Adults with Inflammatory Bowel Diseases
Bibliographic record
Abstract
BACKGROUND: The use of biologics to treat inflammatory bowel disease is supported by robust randomized controlled trials in both ulcerative colitis and Crohn's disease. Nonetheless, an understanding of the principles of clinical trial design is necessary to extrapolate study findings to clinical practice. METHODS: We conducted a review of inflammatory bowel disease registrational clinical trials of biologics to determine how differences in trial design potentially influence results and interpretation. RESULTS: Registrational trials of biological agents have used diverse patient populations, outcome measures, and designs, which makes comparisons of results among studies difficult. Key differences among trials include patient populations, choice of symptom-based measures or objective outcomes as endpoints, and overall trial design. Additional factors, including analytical methods, can also influence the interpretation of outcomes. CONCLUSIONS: The most robust evidence is derived from comparative effectiveness trials. In the absence of these, clinicians should be aware of the various methodological issues which could impact interpretation of efficacy and safety outcomes, including differences in patient population, study design, and analytic methodology.
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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 | 0.087 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".