Systematic review with meta‐analysis: comparative efficacy of immunosuppressants and biologics for reducing hospitalisation and surgery in Crohn's disease and ulcerative colitis
Bibliographic record
Abstract
INTRODUCTION: Crohn's disease (CD) and ulcerative colitis (UC) have a progressive course leading to hospitalisation and surgery. The ability of existing therapies to alter disease course is not clearly defined. AIM: To investigate the comparative efficacy of currently available inflammatory bowel disease (IBD) therapies to reduce hospitalisation and surgery. METHODS: We conducted a systematic review in MEDLINE/PubMed for randomised controlled trials (RCT) published between January 1980 and May 2016 examining efficacy of biological or immunomodulator therapy in IBD. We performed direct comparisons of pooled proportions of hospitalisation and surgery. Pair-wise comparisons using a random-effects Bayesian network meta-analysis were performed to assess comparative efficacy of different treatments. RESULTS: We identified seven randomised controlled trials (5 CD; 2 UC) comparing three biologics and one immunomodulator with placebo. In CD, anti-TNF biologics significantly reduced hospitalisation [Odds ratio (OR) 0.46, 95% confidence interval (CI) 0.36-0.60] and surgery (OR 0.23, 95% CI 0.13-0.42) compared to placebo. No statistically significant reduction was noted with azathioprine or vedolizumab. Azathioprine was inferior to both infliximab and adalimumab in preventing CD-related hospitalisation (>97.5% probability). Anti-TNF biologics significantly reduced hospitalisation (OR 0.48, 95% CI 0.29-0.80) and surgery (OR 0.67, 95% CI 0.46-0.97) in UC. There were no statistically significant differences in the pair-wise comparisons between active treatments. CONCLUSIONS: In CD and UC, anti-TNF biologics are efficacious in reducing the odds of hospitalisation by half and surgery by 33-77%. Azathioprine and vedolizumab were not associated with a similar improvement, but robust conclusions may be limited due to paucity of RCTs.
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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 | 0.022 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.045 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".