Comparative effectiveness of guselkumab in psoriatic arthritis: updates to a systematic literature review and network meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The IL-23 p19-subunit inhibitor guselkumab has been previously compared with other targeted therapies for PsA through network meta-analysis (NMA). The objective of this NMA update was to include new guselkumab COSMOS trial data, and two key comparators: the IL-23 inhibitor risankizumab and the Janus kinase (JAK) inhibitor upadacitinib. MATERIAL AND METHODS: A systematic literature review was conducted to identify randomized controlled trials up to February 2021. A hand-search identified newer agents up to July 2021. Bayesian NMAs were performed to compare treatments on ACR response, Psoriasis Area and Severity Index (PASI) response, modified van der Heijde-Sharp (vdH-S) score, and serious adverse events (SAEs). RESULTS: For ACR 20, guselkumab 100 mg every 8 weeks (Q8W) and every 4 weeks (Q4W) were comparable (i.e. overlap in credible intervals) to most other agents, including risankizumab, upadacitinib, subcutaneous TNF inhibitors and most IL-17A inhibitors. For PASI 90, guselkumab Q8W and Q4W were better than multiple agents, including subcutaneous TNF and JAK inhibitors. For vdH-S, guselkumab Q8W was similar to risankizumab, while guselkumab Q4W was better; both doses were comparable to most other agents. Most agents had comparable SAEs. CONCLUSIONS: Guselkumab demonstrates better skin efficacy than most other targeted PsA therapies, including upadacitinib. For vdH-S, both guselkumab doses are comparable to most treatments, with both doses ranking higher than most, including upadacitinib and risankizumab. Both guselkumab doses demonstrate comparable ACR responses to most other agents, including upadacitinib and risankizumab, and rank favourably in the network for SAEs.
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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 | 0.059 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".