Efficacy of Biological Therapies in Inflammatory Bowel Disease: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: Crohn's disease (CD) and ulcerative colitis (UC) are inflammatory disorders of the gastrointestinal tract of unknown etiology. Evidence for treatment of the condition with biological therapies exists, but no systematic review and meta-analysis has examined this issue in its entirety. METHODS: MEDLINE, EMBASE, and the Cochrane central register of controlled trials were searched (through to December 2010). Trials recruiting adults with active or quiescent CD or UC and comparing biological therapies (anti-tumor necrosis factor-α (TNFα) antibodies or natalizumab) with placebo were eligible. Dichotomous symptom data were pooled to obtain relative risk (RR) of failure to achieve remission in active disease and RR of relapse of activity in quiescent disease once remission had occurred, with a 95% confidence interval (CI). RESULTS: The search strategy identified 3,061 citations, 27 of which were eligible. Anti-TNFα antibodies and natalizumab were both superior to placebo in inducing remission of luminal CD (RR of no remission=0.87; 95% CI 0.80-0.94 and RR=0.88; 95% CI 0.83-0.94, respectively). Anti-TNFα antibodies were also superior to placebo in preventing relapse of luminal CD (RR of relapse=0.71; 95% CI 0.65-0.76). Infliximab was superior to placebo in inducing remission of moderate to severely active UC (RR=0.72; 95% CI 0.57-0.91). CONCLUSIONS: Biological therapies were superior to placebo in inducing remission of active CD and UC, and in preventing relapse of quiescent CD.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.034 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".