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Record W2071366164 · doi:10.3899/jrheum.140106

Restarting Biologics and Management of Patients with Flares of Inflammatory Rheumatic Disorders or Psoriasis During Active Tuberculosis Treatment

2014· review· en· W2071366164 on OpenAlexvenueno aff
Fabrizio Cantini, Francesca Prignano, Delia Goletti

Bibliographic record

VenueJournal of Rheumatology Supplement · 2014
Typereview
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsoriatic arthritisPsoriasisEtanerceptAnkylosing spondylitisRheumatoid arthritisTuberculosisDiseaseAntirheumatic drugsAdalimumabTumor necrosis factor alphaAntirheumatic AgentsDermatologyImmunologyIntensive care medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

Our aim was to review the evidence concerning optimal timing for restarting biologics in patients with active tuberculosis (TB), and the management of relapsing rheumatoid arthritis (RA), psoriatic arthritis (PsA), ankylosing spondylitis (AS), and psoriasis during treatment for TB. Few or no indications are available for 2 important challenges for clinicians: the timing for restarting biologics in patients with TB reactivation and the management of the underlying disorder. In the absence of clear evidence, guidelines and experts suggest restarting anti-tumor necrosis factor-α (TNF-α) agents after completion of an active TB therapy course, but no indications are available on the appropriate management of patients with flares of underlying rheumatic disease or psoriasis. Among anti-TNF-α agents, etanercept is associated with the lowest risk of TB reactivation, and non-anti-TNF-α biologics and several nonbiologic drugs are associated with low/no risk of TB reactivation. Therefore, for patients with relapsing RA, PsA, AS, or psoriasis during TB treatment we propose a therapeutic schedule modulated by disease activity and individual single drug-related TB risk.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.301
Teacher spread0.285 · 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 designOther design
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

Citations22
Published2014
Admission routes1
Has abstractyes

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