114. SECUKINUMAB PROVIDES SUSTAINED IMPROVEMENTS IN THE SIGNS AND SYMPTOMS OF ACTIVE ANKYLOSING SPONDYLITIS: 2-YEAR RESULTS FROM A PHASE 3 TRIAL WITH SUBCUTANEOUS LOADING AND MAINTENANCE DOSING (MEASURE 2)
Bibliographic record
Abstract
Background: MEASURE 2, a Phase 3 trial (NCT01649375), demonstrated secukinumab improved signs and symptoms of ankylosing spondylitis (AS) over 52 weeks. Presented is an update on the long-term (104 weeks) efficacy and safety data. Methods: 219 subjects with active AS, classified by modified New York criteria, despite NSAIDs therapy, were randomized to subcutaneous (s.c.) secukinumab 150 or 75 mg or placebo at Weeks 0, 1, 2, and 3, and every 4 weeks (q4w) from Week 4. At Week 16, placebo-treated subjects were re-randomized to secukinumab 150 or 75 mg s.c. q4w. At baseline, 39% of subjects had an inadequate response/intolerance to prior anti-TNF therapy (anti-TNF-IR). Primary endpoint was ASAS20 response rates at Week 16. Secondary endpoints included ASAS40, hsCRP, ASAS5/6, BASDAI, SF-36 PCS and ASAS partial remission. Endpoints were assessed through Week 104, with multiple imputation for binary variables and a mixed-model repeated measures for continuous variables. Analyses stratified by anti-TNF history were pre-specified and are reported as observed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".