Association of Serum Tocilizumab Trough Concentrations with Clinical Disease Activity Index Scores in Adult Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine whether serum trough concentrations of tocilizumab (TCZ) administered as a fixed-dose subcutaneous (SC) injection for the treatment of rheumatoid arthritis (RA) are associated with disease activity responses. METHODS: We analyzed datasets from the Israeli branch of the multinational TOZURA study, which evaluated a weekly subcutaneous TCZ treatment regimen in a real-life clinical setting. Generalized estimating equations (GEE) were used to evaluate associations between the TCZ levels and the study outcomes. Linear models and GEE were used to evaluate associations between patient characteristics and TCZ levels. RESULTS: A significant association between the TCZ concentrations and the change in the Clinical Disease Activity Index (CDAI) score was observed. In a multivariate binary GEE model, every increase of 10 µg/ml in the concentration of TCZ was associated with being in a state of CDAI remission or low disease activity (OR 1.41) versus a moderate/high disease activity state. An OR of 1.52 was associated with being in a state of Health Assessment Questionnaire-Disability Index remission. In univariate linear models, there was an inverse association between body mass index (BMI) and improvement in the CDAI score, and the BMI score was associated with lower TCZ concentrations. Patients who weighed > 100 kg had lower TCZ concentrations. CONCLUSION: In the first 24 weeks of treatment with SC TCZ injections, TCZ concentrations were associated with clinical improvement, while body weight and BMI were inversely associated with TCZ concentrations. Personalizing the dose of SC TCZ to body weight may improve outcomes of clinical disease activity in patients with RA.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".