Guselkumab provides sustained domain-specific and comprehensive efficacy using composite indices in patients with active psoriatic arthritis
Bibliographic record
Abstract
OBJECTIVES: To evaluate the efficacy of guselkumab for the treatment of active PsA utilizing composite indices. METHODS: Data were pooled from the phase 3 DISCOVER-1 (n = 381) and DISCOVER-2 (n = 739) studies. In both studies, patients were randomized 1:1:1 to subcutaneous guselkumab 100 mg every 4 weeks (Q4W); guselkumab 100 mg at week 0, week 4, then Q8W; or placebo Q4W with crossover to guselkumab 100 mg Q4W at week 24. Composite indices used to assess efficacy through week 52 included Disease Activity Index for Psoriatic Arthritis (DAPSA), Psoriatic Arthritis Disease Activity Score (PASDAS), minimal disease activity (MDA), and very low disease activity (VLDA). Through week 24, treatment failure rules were applied. Through week 52, non-responder imputation was used for missing data. RESULTS: Greater proportions of guselkumab- than placebo-treated patients achieved DAPSA low disease activity (LDA) and remission, PASDAS LDA and VLDA, MDA, and VLDA at week 24 vs placebo (all unadjusted P < 0.05). At week 52, in the guselkumab Q4W and Q8W groups, respectively, response rates were as follows: DAPSA LDA, 54.2% and 52.5%; DAPSA remission, 18.2% and 17.6%; PASDAS LDA, 45.3% and 41.9%; PASDAS VLDA, 16.9% and 19.5%; MDA, 35.9% and 30.7%; and VLDA, 13.1% and 14.4%. In the placebo-crossover-to-guselkumab group, response rates for all composite indices increased after patients switched to guselkumab, from week 24 through week 52. CONCLUSION: Treatment with guselkumab provided robust and sustained benefits across multiple PsA domains through 1 year, indicating that guselkumab is an effective therapy for the diverse manifestations of PsA. TRIAL REGISTRATION: NCT03162796; NCT03158285.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".