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Record W4403384077 · doi:10.56782/pps.262

Mirikizumab – a new option in treatment of inflammatory bowel diseases

2024· article· en· W4403384077 on OpenAlexaboutno aff
Jakub Olszewski, Katarzyna Kozon, Magdalena Sitnik, Katarzyna Herjan, Karolina Mikołap, Bartłomiej Gastoł, M Bara, Piotr Armański, Marcin Sawczuk

Bibliographic record

VenueProspects in Pharmaceutical Sciences · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsInflammatory Bowel DiseasesMedicineInflammatory bowel diseaseIntensive care medicineInternal medicineDisease

Abstract

fetched live from OpenAlex

ABSTRACT Mirikizumab is a humanized monoclonal antibody targeting the p19 subunit of interleukin IL-23. Over the past few years, it has been the subject of clinical trials as a potential new treatment for inflammatory bowel diseases, including ulcerative colitis and Crohn's disease. Additionally, mirikizumab has been investigated in clinical trials as a potential treatment for plaque psoriasis. The results of clinical trials for mirikizumab in treating ulcerative colitis led to its approval in the European Union, the United States, Canada, and Japan for treating adult patients with moderately to severely active ulcerative colitis. Despite promising clinical trial results, mirikizumab has not yet been approved for the treatment of Crohn's disease. This review focuses on summarizing the findings from clinical trials of mirikizumab in the treatment of inflammatory bowel diseases. Information is sourced from scientific papers available on PubMed, by searching for "mirikizumab" and “IL-23” and published to march 2024, as well as from published results of clinical trials concerning mirikizumab. KEYWORDS: mirikizumab, IL-23, ulcerative colitis

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)0.000
metaresearch head score (Gemma)0.000
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.340
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; 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

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