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Record W4406542819 · doi:10.1136/flgastro-2024-102830

Landscape of anti-IL-23 therapy in inflammatory bowel disease: recent advances

2025· article· en· W4406542819 on OpenAlexaff
Michael Colwill, Jennifer Clough, Samantha Baillie, Kamal Patel, Laurent Peyrin‐Biroulet, Sailish Honap

Bibliographic record

VenueFrontline Gastroenterology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsMedicineInflammatory bowel diseaseDiseaseIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Inflammatory bowel disease (IBD) commonly requires advanced therapies to induce and maintain durable remission. Interleukin (IL)-23 is a proinflammatory heterodimeric cytokine composed of a p40 subunit, which is shared with IL-12 and a unique p19 subunit. There are multiple streams of evidence that implicate IL-23 in the pathogenesis and pathophysiology of IBD and it has emerged as a crucial therapeutic target in IBD and several immune-mediated inflammatory diseases. Risankizumab, mirikizumab and guselkumab are monoclonal antibodies that selectively target the IL-23p19 subunit and offer a novel mechanism of action. This narrative review summarises key efficacy and safety data from the clinical trial programmes, highlights practical implications for their use in IBD, and reviews available data regarding their positioning in the rapidly expanding landscape of IBD treatments.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
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 head0.004
GPT teacher head0.234
Teacher spread0.230 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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