Evaluation of serious infections, including <i>Mycobacterium tuberculosis</i>, during treatment with biologic disease-modifying anti-rheumatic drugs: does line of therapy matter?
Bibliographic record
Abstract
OBJECTIVES: This study aimed to evaluate if and how the incidence of serious infection (SI) and active tuberculosis (TB) differ among seven biologic DMARDs (bDMARDs) in patients with RA considering the line of therapy. METHODS: Patients with RA from the British Society for Rheumatology Biologics Register for Rheumatoid Arthritis (BSRBR-RA) cohort who initiated etanercept, certolizumab, infliximab, adalimumab, abatacept, rituximab or tocilizumab from the first to fifth line of therapy were included. Follow-up extended up to 3 years. The primary outcome was SI and the secondary outcome was TB. Event rates were calculated and compared using Cox proportional hazards models, controlling for confounding with inverse probability of treatment weights. Comparisons were made overall and stratified by line of therapy. Sensitivity analysis was restricted to all treatment courses from 2009 (tocilizumab availability) until the end of the study (2018). RESULTS: Among 33 897 treatment courses (62 513 patient-years) the incidence of SI was 4.4/100 patient-years (95% CI 4.2, 4.5). After adjustment, hazards ratios (HRs) of SI were slightly higher with adalimumab and infliximab compared with etanercept. However, no clear pattern was observed when stratifying by line of therapy in terms of incidence rate or HR. Sensitivity analyses showed similar HRs among these treatments. Regarding TB, all 49 cases occurred during the first three lines of treatment and rarely since 2009. CONCLUSION: The risk of serious infections does not appear to be influenced by the line of therapy in patients with RA. However, the risk of TB seems to be more frequent during the initial lines of treatment or prior to 2009.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".