Real-World Incidence and Determinants of Infection in Patients With Rheumatoid Arthritis Treated With Golimumab After a Median Follow-Up Time of 27 Months
Bibliographic record
Abstract
OBJECTIVE: To characterize the long-term incidence of infection in patients with rheumatoid arthritis (RA) treated with subcutaneous golimumab (GOL) in Canadian routine care, assess the effect of infections on GOL retention, and explore factors associated with infection incidence. METHODS: Patients with RA enrolled in the Biologic Treatment Registry Across Canada (BioTRAC) initiating GOL treatment were included. The incidence density rates (IDRs) of total infection (TI), serious infection (SI), and nonserious infection (NSI) were calculated for the overall follow-up (90 months) and by 6-month intervals. Determinants of infection over time or within the first 6 months were explored using generalized estimating equation models and logistic regression, respectively. RESULTS: Five hundred thirty patients were included; mean baseline age was 57.7 years and RA duration was 8.0 years. Over an average follow-up of 27.0 months, the IDR for TIs was 35.1 events per 100 person-years (PYs), the majority occurring during the first 6 months; IDRs for NSIs and SIs were 32.9 and 2.2 events per 100 PYs, respectively. No predictors were identified for infection incidence within 6 months. Comorbid pulmonary disease was associated with significantly higher odds of TIs and NSIs over time, whereas higher age and high corticosteroid (CS) dose (> 5 mg/day) predicted higher odds of SIs. Incidence of SIs, but not NSIs, was associated with significantly higher odds of GOL discontinuation. CONCLUSION: Long-term GOL treatment was associated with relatively low infection rates, most being nonserious and occurring during the first 6 months. Pulmonary disease, higher age, and high CS dose were identified as significant predictors of infections. SIs, but not NSIs, predicted higher odds of GOL discontinuation. (ClinicalTrials.gov: NCT00741793).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".