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Record W2059171219 · doi:10.3899/jrheum.120255

Should We Consider Tumor Necrosis Factor as the Only Target in Spondyloarthritides?

2012· review· en· W2059171219 on OpenAlexvenueno aff
Gianfranco Ferraccioli, Elisa Gremese

Bibliographic record

VenueJournal of Rheumatology Supplement · 2012
Typereview
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTumor necrosis factor alphaInflammationBlockadeImmunologySacroiliitisBioinformaticsCancer researchArthritisInternal medicineReceptor

Abstract

fetched live from OpenAlex

Understanding the biology of inflammation occurring at the entheseal-bone insertion has led to a better knowledge of the main drivers of inflammation in spondyloarthropathies. The clinical efficacy of tumor necrosis factor-α (TNF-α) blockers strongly supports the idea that TNF-α is a key molecule. Yet 40% of patients do not respond appropriately, indicating that other pathways are likely involved in these illnesses. Targeting T cells through a blockade of costimulating (CD28) molecules does not help, and in experimental models of sacroiliitis, targeting interleukin 6 (IL-6) did not provide any useful evidence. Immunohistological and functional data suggest that B cells, Th17, or IL-17A might be important, and indeed preliminary data concerning drugs targeting B cells and IL-17A seem to suggest clinical benefits.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.075
GPT teacher head0.365
Teacher spread0.289 · 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
GenreReview

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

Citations2
Published2012
Admission routes1
Has abstractyes

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