Efficacy of Subcutaneous Secukinumab in Patients with Active Psoriatic Arthritis Stratified by Prior Tumor Necrosis Factor Inhibitor Use: Results from the Randomized Placebo-controlled FUTURE 2 Study
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of prior tumor necrosis factor inhibitor (TNFi) therapy on secukinumab efficacy in psoriatic arthritis (PsA). METHODS: Patients were randomized to secukinumab 300 mg, 150 mg, 75 mg, or placebo. RESULTS: American College of Rheumatology 20 responses at Week 24 with secukinumab 300 mg, 150 mg, 75 mg, and placebo were 58.2%, 63.5%, 36.9%, and 15.9% in TNFi-naive (n = 258), and 45.5%, 29.7%, 14.7%, and 14.3% in TNFi-exposed patients (n = 139), respectively. Week 52 responses with secukinumab 300 mg, 150 mg, and 75 mg were 68.7%, 79.4%, and 58.5% in TNFi-naive, and 54.5%, 37.8%, and 35.3% in TNFi-exposed patients, respectively. CONCLUSION: Secukinumab was efficacious in TNFi-naive and TNFi-exposed patients with PsA, with greatest improvements in TNFi-naive patients.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".