Comparative effects of biological and targeted synthetic DMARDs on incident chronic kidney disease in patients with rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVES: The impact of individual biological/targeted synthetic DMARD (b/tsDMARD) on kidney function in patients with RA remains unclear. This study aimed to determine the comparative effects of b/tsDMARDs on chronic kidney disease (CKD) incidence in patients with RA. METHODS: This multicentre cohort study included patients with RA who had baseline estimated glomerular filtration rate (eGFR) of ≥60 ml/min/1.73 m2 and started a TNF inhibitor (TNFi), cytotoxic T-lymphocyte-associated antigen-4-Ig (CTLA4-Ig), interleukin-6 receptor inhibitor, or Janus kinase inhibitor (JAKi) in Japan. Multiple propensity score-based inverse probability weighting (IPW) was used to adjust confounders. The incidence of CKD was compared among b/tsDMARDs using IPW mixed-effect Cox proportional hazards models and linear mixed-effect models with IPW-examined trajectories of eGFR. RESULTS: Among 2187 patients with 3068 treatment courses and up to 11 years of follow-up, CKD occurred in 275 cases. Compared with the CTLA4-Ig group, the TNFi group had a significantly lower CKD incidence [hazard ratio (HR) 0.67, 95% CI 0.46-0.97, P = 0.04], whereas the JAKi group had a significantly higher incidence (HR 2.16, 95% CI 1.23-3.79, P = 0.01). The trajectory of eGFR was significantly greater in the JAKi group than in the CTLA4-Ig group (CTLA4-Ig: -1.28 ml/min/1.73 m2/year, JAKi: -2.29 ml/min/1.73 m2/year, P < 0.001). CONCLUSIONS: TNFi use was associated with reduced CKD incidence, whereas JAKi showed a less protective association for kidney function in patients with RA.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".