Development of Nephrotic Syndrome in a Patient with Rheumatoid Arthritis Treated with Certolizumab
Bibliographic record
Abstract
To the Editor: Tumor necrosis factor-α (TNF-α) inhibitors have become widely accepted and are vital in the treatment of rheumatoid arthritis (RA) and other autoimmune inflammatory diseases1. Their target, TNF-α, promotes inflammation through a variety of mechanisms, including cytokine and chemokine expression, and suppression of regulatory T cells2. Although an important therapy for many inflammatory diseases, TNF-α inhibitors may contribute to serious adverse effects, including infection, heart failure, and hematologic and nervous system disorders1. Here we describe the first documented case, to our knowledge, of nephrotic syndrome with biopsy-proven membranous glomerulonephritis (GN) due to certolizumab in an individual with RA. A 63-year-old female with nodular, erosive, seropositive RA for over 15 years with suboptimal control with hydroxychloroquine (HCQ) monotherapy and previous intolerance to methotrexate presented to the clinic for a second opinion. Her rheumatoid factor was 95 IU/ml and anti-citrullinated protein antibody was > 250. Given her active inflammatory disease with a 28-joint Disease Activity Score (DAS28) of 5.89, we began treatment with adalimumab (ADA). She responded well initially, but effectiveness waned over 12 months with intermittent discontinuation … Address correspondence to Dr. R. Butendieck, Mayo Clinic, 4500 San Pablo Road, Jacksonville, Florida 32224, USA. E-mail: Butendieck.Ronald{at}mayo.edu
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".