Effect of Secukinumab on the Different GRAPPA-OMERACT Core Domains in Psoriatic Arthritis: A Pooled Analysis of 2049 Patients
Bibliographic record
Abstract
OBJECTIVE: To compare the efficacy of secukinumab with that of placebo across the updated Group for Research and Assessment of Psoriasis and Psoriatic Arthritis and Outcome Measures in Rheumatology (GRAPPA-OMERACT) individual psoriatic arthritis (PsA) core domains using pooled data from 4 phase III PsA studies and 1 phase III ankylosing spondylitis (AS) study. METHODS: Data were pooled from 2049 patients with PsA participating in 4 on-label phase III PsA studies (FUTURE 2-5), and the efficacy of each GRAPPA-OMERACT PsA core domain (musculoskeletal disease activity, skin disease activity, pain, patient's global assessment, physical function, health-related quality of life, fatigue, and systemic inflammation) was assessed using multiple measures and definitions specific to each domain. The MEASURE 2 study, a phase III clinical trial in patients with AS, was used to assess improvement in spine symptoms at Week 16. RESULTS: Treatment with secukinumab demonstrated robust and consistent efficacy across all GRAPPA-OMERACT PsA core domains, with secukinumab 300 mg showing the greatest response rates across most PsA core domains compared with placebo at Week 16. Notably, among patients treated with secukinumab 300 mg, 34.3% and 19.5% achieved complete resolution of swollen and tender joint counts, respectively; 53.2% and 61.5% achieved complete resolution of enthesitis and dactylitis, respectively; and 33.2% achieved 100% improvement in Psoriasis Area and Severity Index (all p < 0.05 vs placebo); similar improvements were shown for all other core domains. CONCLUSION: This analysis suggests that secukinumab can benefit people with PsA across the clinical phenotypic spectrum commonly encountered in this disease.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".