Comparative safety and benefit-risk profile of biologics and oral treatment for moderate-to-severe plaque psoriasis: A network meta-analysis of clinical trial data
Bibliographic record
Abstract
BACKGROUND: The comparative safety and benefit-risk profiles of moderate-to-severe psoriasis treatment have not been well studied. OBJECTIVE: To compare the short-term (12-16 weeks) and long-term (48-56 weeks) safety and benefit-risk profiles of moderate-to-severe psoriasis treatments. METHODS: A systematic literature review of phase II-IV randomized controlled trials of moderate-to-severe psoriasis treatments was conducted (cutoff: July 1, 2020). Any adverse events (AEs), any serious AEs, and AEs leading to treatment discontinuation were compared using Bayesian network meta-analyses (NMAs). RESULTS: Fifty-two and 7, respectively, randomized controlled trials were included in the short- and long-term NMAs, respectively. In the short-term NMA, the rates of any AEs were the lowest for tildrakizumab (posterior median: 46.0%), certolizumab (46.2%), and etanercept (49.1%). The rates of any serious AE were the lowest for certolizumab (0.8%), risankizumab (1.2%), and etanercept (1.6%). The rates of AEs leading to treatment discontinuation were the lowest for risankizumab (0.5%), tildrakizumab (1.0%), and guselkumab (1.5%). In the long-term NMA, risankizumab had the lowest rates of all 3 outcomes (67.5%, 4.4%, and 1.0%, respectively) and the most favorable benefit-risk profile. LIMITATIONS: The results may not be generalizable to real-world populations. CONCLUSIONS: Anti-interleukin 23 agents were associated with low rates of safety events. Risankizumab had the most favorable benefit-risk profile in the long term.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".