Effectiveness, Safety, and Predictors of Good Clinical Response in 1250 Patients Treated with Adalimumab for Active Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: We evaluated the effectiveness and safety of adalimumab in a large cohort of patients with active ankylosing spondylitis (AS) and identified clinical predictors of good clinical response. METHODS: Patients with active AS [Bath AS Disease Activity Index (BASDAI)>or=4] received adalimumab 40 mg every other week in addition to their standard antirheumatic therapies in a multinational 12-week, open-label study. We used 3 definitions of good clinical response: 50% improvement in the BASDAI (BASDAI=50), 40% improvement in the ASsessments of SpondyloArthritis International Society criteria (ASAS40), or ASAS partial remission. Response predictors were determined by logistic regression with backward elimination (selection level 5%). RESULTS: Of 1250 patients, 1159 (92.7%) completed 12 weeks of adalimumab treatment. At Week 12, 57.2% of patients achieved BASDAI 50, 53.7% achieved ASAS40, and 27.7% achieved ASAS partial remission. Important predictors of good clinical response (BASDAI 50, ASAS40, and partial remission) were younger age (p<0.001), and greater C-reactive protein (CRP) concentration (p<or=0.001), HLA-B27 positivity (p<or=0.01), and tumor necrosis factor (TNF) antagonist naivety (p<0.001). CONCLUSION: Adalimumab was effective in this large cohort of patients with AS, with more than half of patients achieving a BASDAI 50 or ASAS40 response and more than a quarter of patients reaching partial remission at Week 12.Younger age, greater CRP concentrations, HLA-B27 positivity, and TNF antagonist naivety were strongly associated with BASDAI 50, ASAS40, and partial remission responses. ClinicalTrials.gov identifier: NCT00478660.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| 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".