Efficacy of Brodalumab for Moderate to Severe Plaque Psoriasis: A Canadian Network Meta-Analysis
Bibliographic record
Abstract
Background Several treatments for plaque psoriasis are available, but it remains challenging for physicians to make informed treatment decisions due to a lack of head-to-head trials. Objectives This network meta-analysis (NMA) compares the efficacy of brodalumab to other biologic agents in Canada for moderate-to-severe plaque psoriasis. Methods A systematic literature review of randomized controlled trials (RCTs) published before October 2017 was conducted to populate the NMA. Comparators included etanercept, infliximab, adalimumab, ustekinumab, secukinumab, ixekizumab, guselkumab, and placebo. The primary outcome was the psoriasis area and severity index (PASI) response at the end of induction phase. A random effects Bayesian multinomial likelihood and probit link model analyzed PASI 75, 90, and 100 responses. Inconsistency and heterogeneity were assessed. Sensitivity analyses were conducted to explore potential effect modifiers like baseline PASI score, age, and weight. Results A total of 43 RCTs were included. Brodalumab 210 mg had significantly better PASI response than etanercept, ustekinumab, adalimumab, secukinumab, and guselkumab and comparable responses to infliximab and ixekizumab. Relative risk of PASI 90 response for brodalumab varied from 2.84 (95% credible interval [CrI]: 2.35-3.52, P < .05) to 0.99 (95% CrI: 0.88-1.11, ns) compared to etanercept and ixekizumab. This was similar across PASI 75 responses, but a larger relative risk between brodalumab and all comparators except ixekizumab was observed for PASI 100. No significant heterogeneity or inconsistencies were identified. The results were consistent across sensitivity analyses, indicating robustness of the results. Conclusion Brodalumab 210 mg has efficacy superior to most biologic agents for moderate-to-severe plaque psoriasis in Canada.
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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 | 0.029 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".