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Record W2099555993 · doi:10.3899/jrheum.090307

Sarcoidosis During Anti-Tumor Necrosis Factor-α Therapy: No Relapse After Rechallenge

2009· letter· en· W2099555993 on OpenAlexvenueno aff
DEBORAH van der STOEP, Gert‐Jan Braunstahl, J. van Zeben, Jacques M.G.W. Wouters

Bibliographic record

VenueThe Journal of Rheumatology · 2009
Typeletter
Languageen
FieldMedicine
TopicSarcoidosis and Beryllium Toxicity Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAdalimumabInfliximabEtanerceptSarcoidosisRheumatoid arthritisAdverse effectNeurosarcoidosisTumor necrosis factor alphaDermatologyErythema nodosumArthritisInternal medicineImmunologyDisease

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.034
GPT teacher head0.290
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2009
Admission routes1
Has abstractyes

Explore more

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