Sarcoidosis During Anti-Tumor Necrosis Factor-α Therapy: No Relapse After Rechallenge
Bibliographic record
Abstract
To the Editor: Anti-tumor necrosis factor (TNF)-α agents such as infliximab, adalimumab, and etanercept are more and more widely used. With this we see a growing number of case reports describing adverse events that occur during anti-TNF-α therapy. One adverse event that has been reported increasingly is the development of sarcoidosis1. This raises many questions. TNF-α plays an important role in the formation of granulomas. Based on this, specific anti-TNF-α agents are supposed to be effective in the treatment of granulomatous diseases such as Crohn’s disease and sarcoidosis. Although current evidence supports the efficacy of infliximab in Crohn’s disease, data regarding anti-TNF-α in sarcoidosis have been conflicting2,3. We describe 2 cases in which sarcoidosis occurred during respectively adalimumab and etanercept treatment, both for rheumatoid arthritis. After cure/stabilization of the sarcoidosis both patients were rechallenged with the anti-TNF-α agent they took originally. The first patient was a 55-year-old woman with rheumatoid arthritis (RA) for 5 years. Eight months after treatment with adalimumab 40 mg every 2 weeks, she developed erythema nodosum-like lesions … Address correspondence to D. van der Stoep. E-mail: dfvanderstoep{at}gmail.com
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.002 | 0.000 |
| 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".