Beneficial effects of adalimumab on biomarkers reflecting structural damage in patients with ankylosing spondylitis.
Bibliographic record
Abstract
OBJECTIVE: We analyzed the effects of adalimumab on biomarkers predictive of structural damage in inflammatory arthritis. METHODS: In a 24-week randomized controlled trial, patients with active ankylosing spondylitis (AS) received adalimumab 40 mg or placebo every other week. Efficacy measures included ASsessment in Ankylosing Spondylitis International Working Group response, Bath AS Disease Activity Index (BASDAI), Total Back Pain, Bath AS Functional Index, C-reactive protein (CRP), and patient's global assessment of disease activity. Urinary type II collagen C-telopeptides (CTX-II), serum type I collagen N-telopeptides (NTX), and serum metalloproteinase-3 (MMP-3) were assessed using ELISA for treatment-group differences at baseline, 12, and 24 weeks. We determined correlations between changes in biomarkers and AS efficacy outcomes. RESULTS: A total of 82 patients (38 adalimumab, 44 placebo) enrolled. At 12 and 24 weeks, significant reductions in urinary CTX-II and MMP-3, but not NTX concentrations, were observed for adalimumab versus placebo (p<0.001). Significant baseline correlations were noted between CRP and CTX-II (r=0.71), MMP-3 (r=0.45), and NTX (r=0.37) (p<or=0.001), as well as between CTX-II and NTX (r=0.49; p<0.0001). Changes in CTX-II and MMP-3 at 12 weeks correlated significantly with changes in BASDAI (r=0.31 and 0.33), and CRP (r=0.40 and 0.43) (p<or=0.005). Change in CTX-II at 12 weeks also correlated significantly with change in MMP-3 (r=0.41; p<0.0001). CONCLUSION: Adalimumab suppresses biomarkers that reflect matrix turnover in patients with AS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.002 | 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".