Biologics in inflammatory bowel disease: what are the data?
Bibliographic record
Abstract
BACKGROUND: Over the last decade, biologics have gained an important place for the treatment of moderate to severe inflammatory bowel disease (IBD), and many randomized control trials have evaluated their efficacy. AIM: The goal of this review is to analyze the results of these trials and to highlight the evidence and indications emerging from these studies for their implementation in the management of IBD patients. METHODS: A PubMed search was realized to screen high-quality clinical trials studying biologic agents currently available in clinics for the treatment of IBD. Words used were: "infliximab," "adalimumab," "certolizumab," "golimumab," "natalizumab," "vedolizumab," "ustekinumab," "azathioprine," "methotrexate," "Crohn's disease," and "ulcerative colitis." RESULTS: In Crohn's disease, studies supporting induction and maintenance therapies were documented for infliximab, adalimumab, certolizumab, natalizumab, vedolizumab, and ustekinumab. Infliximab, adalimumab, and certolizumab have evidences for fistulizing Crohn's disease and only infliximab and adalimumab have evidences for mucosal healing. In ulcerative colitis, studies supporting induction, maintenance, and mucosal healing were found with infliximab, adalimumab, golimumab, and vedolizumab. Only infliximab was associated with evidences for combination therapy with thiopurine and acute severe colitis in ulcerative colitis. CONCLUSION: Management with biologics in IBD patients is well validated by high-quality clinical trials.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".