A Case of Minimal Change Disease in a Patient with Rheumatoid Arthritis Treated with Certolizumab
Bibliographic record
Abstract
Manifestations of renal disease have been reported to develop after tumor necrosis factor-α (TNF-α) inhibition, and they should be considered as a possible complication as TNF-α inhibitors become more prevalent in the treatment of autoimmune inflammatory diseases. We report a case of minimal change disease (MCD) that developed in a woman receiving the TNF-α inhibitor certolizumab and was resolved with high-dose steroids and discontinuation of TNF-α blockade. Ethics approval was waived by the Mayo Clinic Institutional Review Board; the patient’s written informed consent was obtained. A 49-year-old woman with a background history of rheumatoid arthritis (RA) and Sjögren syndrome presented with a 1-week history of foamy urine, peripheral edema, and 15-kg weight gain. For her RA, she was taking prednisone 5 mg daily and certolizumab 400 mg monthly; the latter had been started 6 months prior to presentation. Physical examination findings demonstrated 2+ pitting edema to the knees … Address correspondence to Dr. S.R. Hwang, Mayo Clinic, 200 1st St. SW, Rochester, Minnesota 55902, USA. E-mail: hwang.steven{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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| 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".