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Record W2115987656 · doi:10.3899/jrheum.140102

Adalimumab, Etanercept, Infliximab, and the Risk of Tuberculosis: Data from Clinical Trials, National Registries, and Postmarketing Surveillance

2014· review· en· W2115987656 on OpenAlexvenueno aff
Fabrizio Cantini, Laura Niccoli, Delia Goletti

Bibliographic record

VenueJournal of Rheumatology Supplement · 2014
Typereview
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAdalimumabInfliximabEtanerceptRheumatoid arthritisInternal medicineRandomized controlled trialTuberculosisTumor necrosis factor alphaPathology

Abstract

fetched live from OpenAlex

This review evaluates the risk of tuberculosis (TB), adherence with recommendations for TB prevention, and host-related risk in patients with rheumatoid arthritis (RA), psoriatic arthritis, and ankylosing spondylitis receiving infliximab (IFX), adalimumab (ADA), and etanercept (ETN) through an analysis of phase III randomized controlled trials (RCT), postmarketing surveillance, and national registries. Ten (0.21%) TB cases occurred among 4590 patients in 16 RCT of IFX, 9 (0.12%) among 7009 patients in 21 RCT of ADA, and 4 (0.05%) among 7741 patients in 26 RCT of ETN. Overall, 19/23 (83%) TB cases occurred in patients with RA. Data from national registries and postmarketing surveillance showed an increased risk of TB in patients receiving any of the 3 anti-tumor necrosis factor (TNF) drugs, with a 3-4 times higher risk associated with IFX and ADA than with ETN. Deviations from recommended TB prevention procedures were observed in up to 80% of patients, and most registries did not include data on host-related risk factors for TB. TB occurrence was reduced in recent RCT but not in real-life practice. TB risk was lower for ETN than for monoclonal antibody anti-TNF agents. More complete data collection, including host-related TB risk factors, is advisable to avoid biased results.

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)0.041
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.899
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0410.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.134
GPT teacher head0.451
Teacher spread0.317 · 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; both teacher heads agree on what is shown here.

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

Citations120
Published2014
Admission routes1
Has abstractyes

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