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Record W2581513023 · doi:10.1177/1759720x16687481

Baricitinib in rheumatoid arthritis: evidence-to-date and clinical potential

2017· review· en· W2581513023 on OpenAlexaff
Bindee Kuriya, Marc D. Cohen, Ed Keystone

Bibliographic record

VenueTherapeutic Advances in Musculoskeletal Disease · 2017
Typereview
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsMount Sinai HospitalSinai Health System
FundersU.S. Food and Drug Administration
KeywordsMedicineRheumatoid arthritisDosingJanus kinaseDrugClinical trialPharmacologyInternal medicineReceptor

Abstract

fetched live from OpenAlex

Biologics have changed expectation and outcomes for rheumatoid arthritis (RA). However, the optimal duration and sequence of therapy for this disease has yet to be determined. Also, a significant number of patients do not satisfactorily respond to currently available therapies. The Janus kinase (JAK) inhibitors represent a new class of therapies for RA. These drugs work uniquely by inhibiting intracellular pathways thought to be important in the pathogenesis of RA. They are available as oral agents, which is also different from the currently available biologics. Baricitinib has now been evaluated in four phase III clinical trials, and although safety concerns cannot fully be answered until the drug is studied over longer periods of time, the data to date suggest that this drug with its once daily dosing, rapid onset of action and efficacy as monotherapy represents an important addition to the RA therapeutic armamentarium. Further study and experience will better define how baricitinib will be used and by which patients.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.080
GPT teacher head0.458
Teacher spread0.378 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations53
Published2017
Admission routes1
Has abstractyes

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