Effectiveness, Safety, and Predictors of Good Clinical Response in 1250 Patients Treated with Adalimumab for Active Ankylosing Spondylitis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it