171 Secukinumab provides sustained PASDAS related low disease activity in psoriatic arthritis: two year results from the FUTURE 2 study
Bibliographic record
Abstract
Background: Psoriatic Arthritis Disease Activity Score (PASDAS) assesses multiple facets of psoriatic arthritis (PsA) and has been demonstrated to distinguish treatment effect, perform better in statistical terms than traditional joint-only indices and can be used as a treatment target in clinical trials in PsA. Secukinumab provided sustained improvement in the signs and symptoms of PsA over 104 weeks in the FUTURE 2 study. Here, we report the ability of secukinumab to reach and sustain PASDAS-based low-disease activity (LDA) up to 104 weeks in the FUTURE 2 study using a post-hoc exploratory analysis. Methods: 397 patients with active PsA were randomised to subcutaneous (s.c.) secukinumab (300, 150, or 75 mg) or placebo at baseline, weeks 1, 2, 3, and 4 and then every four weeks (q4w). Placebo non-responder and responder patients were re-randomised to secukinumab 300 or 150 mg s.c. q4w from week 16 and 24, respectively. PASDAS is derived from patient and physician global visual analogue scale (VAS) scores, Short Form-36 Physical Component Summary (SF-36 PCS) score, tender and swollen joint counts (TJC68 and SJC66), Leeds Enthesitis Index score, dactylitis count and C-reactive protein (CRP) level and has cut-off points for high-disease activity (HDA; ≥5.4), low-disease activity (LDA; <3.2) and remission (≤1.9). PASDAS was assessed in the overall population and in patients stratified by prior tumour necrosis factor inhibitor (TNFi) use (naive vs. inadequate responder/intolerant [IR]) and disease duration (≤2 years vs. >2 years since diagnosis) and reported using non-mutually exclusive categories at group level and as observed analysis. Only data for approved doses of secukinumab (300/150 mg are shown).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".