Relationship Between Adalimumab Concentrations, Antidrug Antibodies, and Disease Activity in Rheumatoid Arthritis: A Cross-Sectional Observational Study
Bibliographic record
Abstract
Objectives To determine the influence of patient characteristics and disease activity on adalimumab (ADA) concentrations; to assess the relationships between ADA concentrations, the presence of antidrug antibodies (ADAb), and disease activity in rheumatoid arthritis (RA); and to determine the association between cytokine concentrations and ADA concentrations. Methods A cross-sectional study of people with RA receiving ADA for at least 4 weeks was undertaken. Disease activity was assessed by the Disease Activity Score in 28 joints (DAS28), with responders defined as DAS28 ≤ 3.2. Serum and plasma were obtained for ADA concentrations and ADAb, and a panel of cytokines were obtained for a subgroup. ADA concentrations were compared between demographic and clinical subgroups using ANOVA. The independent associations between clinical and demographic features were analyzed using a general linear model. Variables significantly associated with ADA concentrations from the univariate analyses were entered into multivariate analyses. Results Of the 156 participants, 69.2% were female and the mean age was 57.4 (SD 12.7) years. Multivariate analysis revealed that higher C-reactive protein (P< 0.001) and higher weight (P< 0.004) were independently associated with lower ADA concentrations. ADA concentrations were higher in those with DAS28 ≤ 3.2 compared to those with DAS28 > 3.2 (median 10.8 [IQR 6.4-20.8] mg/L vs 7.1 [IQR 1.5-12.6] mg/L,P< 0.001). There was a significant negative correlation between interleukin 6 (IL-6) and ADA concentrations (r= −0.04,P< 0.01). Conclusion ADA concentration correlates negatively with markers of inflammatory disease activity in RA, including IL-6. ADA concentration in the range 5 to 7 mg/L over the dose interval are associated with better disease control.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".