Tumor Necrosis Factor-α Inhibitor-induced Antiglomerular Basement Membrane Antibody Disease in a Patient with Rheumatoid Arthritis: Figure 1.
Bibliographic record
Abstract
To the Editor: Tumor necrosis factor (TNF)–targeted therapy is widely used for various rheumatic diseases. However, severe adverse effects have been reported, with a number of studies reporting the development of vasculitis associated with anti-TNF agents1,2,3. This is the first report, to our knowledge, of a patient with rheumatoid arthritis (RA) who developed antiglomerular basement membrane (anti-GBM) antibody disease during treatment with adalimumab, a fully human immunoglobulin G1 monoclonal antibody against TNF-α. A 69-year-old woman was diagnosed with RA based on the criteria of the American College of Rheumatology/European League Against Rheumatism (2010) at the age of 68 years because she presented with symmetrical small-joint arthritis and was positive for anticitrullinated protein antibodies (ACPA)4. Treatment with methotrexate, bucillamine, and prednisolone was started, but was stopped 5 months later because interstitial lung disease was detected, after which adalimumab was initiated (40 mg biweekly). There were no findings of renal dysfunction, proteinuria/hematuria, or rheumatoid vasculitis before … Address correspondence to Dr. J. Saegusa, Department of Clinical Pathology and Immunology, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-cho, Chuo-ku, Kobe 650-0017, Japan. E-mail: jsaegusa{at}med.kobe-u.ac.jp
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".