Adverse Events and Nocebo Effects in Inflammatory Bowel Disease: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND AND AIMS: Nocebo effects, adverse outcomes occurring in patients receiving inert therapy, contribute to adverse event [AE] reporting in randomized controlled trials [RCTs]. High placebo AE rates may result in inaccurate estimation of treatment-related AEs. We estimate the pooled rate of AEs in patients randomized to placebo compared to active therapy in inflammatory bowel disease [IBD] RCTs. METHODS: MEDLINE, EMBASE and CENTRAL were searched to March 1, 2017 for RCTs of conventional medical therapies for Crohn's disease [CD] or ulcerative colitis [UC]. Rates of AEs, serious AEs [SAEs], AE-related trial withdrawal, infections and worsening IBD were pooled using a random-effects model. RESULTS: We included 124 CD [n = 26 042] and 71 UC RCTs [n = 16 798]. The pooled placebo AE rate was 70.6% (95% confidence interval [CI]: 65.3%, 75.4%) and 54.5% [47.8%, 61.1%] in CD and UC RCTs, respectively. There was no significant risk difference [RD] in AE, SAE or AE-related withdrawal rates between CD patients receiving placebo or active drug. A 1.6% [95% CI: 0.1%, 3.1%] increase in AE rates was observed among UC patients randomized to active therapy. Patients receiving active therapy had a higher risk of infection (RD 1.0% [95% CI: 0.4%, 1.7%] for CD, 2.9% [95% CI: 1.4%, 4.4%] for UC) although a lower risk of worsening CD (RD -3.2% [95% CI: -4.8%, -1.5%]) or UC (RD -3.7% [95% CI: -5.7%, -1.8%]). CONCLUSIONS: AEs are commonly reported by patients randomized to either placebo or active treatment in IBD RCTs. Clinically relevant differences in AE, SAE and AE-related withdrawal were not observed.
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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 | 0.038 | 0.085 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.010 | 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) | 0.003 | 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".