Etanercept‐induced cutaneous and pulmonary sarcoid‐like granulomas resolving with adalimumab
Bibliographic record
Abstract
A 59-year-old female with rheumatoid arthritis on etanercept therapy presented with a 7-cm-large subcutaneous forearm mass. Multiple smaller nodules subsequently developed on the upper and lower extremities. Except for a new cough, the patient was systemically well. Biopsy of the mass showed sarcoidal type granulomatous inflammation with nodular aggregations of non-necrotizing epithelioid histiocytes in the subcutis. A chest computed tomography (CT) scan showed mediastinal adenopathy consistent with pulmonary sarcoidosis. Etanercept was discontinued, and the patient was started on adalimumab for rheumatoid arthritis control. The cutaneous nodules fully resolved in 6 months with no additional treatment. A 4-month follow-up CT scan showed significant regression of mediastinal adenopathy. The patient has since been maintained on adalimumab therapy for 2 years with no recurrence of sarcoid-like manifestations. Biologic response modifiers targeting tumor necrosis factor alpha (TNFα) are effective treatments of chronic inflammatory conditions such as rheumatoid arthritis and psoriasis. TNFα represents a major cytokine in granuloma formation, and TNFα inhibitors are sometimes efficacious in the treatment of sarcoidosis. Paradoxically, there is a small volume of literature implicating TNFα inhibitors in the development of sarcoid-like disease. We present this case to promote the recognition of TNFα inhibitor-induced sarcoidosis and to illustrate the wide clinicopathologic differential of sarcoidal type granulomas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".