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Record W2299990570 · doi:10.1159/000443132

Anti-Integrins in Ulcerative Colitis and Crohn's Disease: What Is Their Place?

2016· review· en· W2299990570 on OpenAlexaff
Reena Khanna, Mahmoud Mosli, Brian G. Feagan

Bibliographic record

VenueDigestive Diseases · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsNatalizumabMedicineVedolizumabUlcerative colitisProgressive multifocal leukoencephalopathyInflammatory bowel diseaseCrohn's diseaseImmunologyDiseaseMultiple sclerosisInflammationMonoclonal antibodyImmune systemAntibodyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Inflammatory bowel diseases (IBD) are a group of heterogeneous conditions, characterized by immune-mediated inflammation of the gastrointestinal tract. Traditionally, medical management of these disorders has been based on use of systemic immunosuppressives. The development of new drugs that selectively inhibit leukocyte trafficking to the gut has the potential to reduce inflammation and minimize systemic toxicities. KEY MESSAGES: In this article, we review the immunology of the gut and the mechanism of action these emerging therapies for IBD. Natalizumab, a monoclonal antibody to the α4 integrin, was approved for the treatment of multiple sclerosis and showed promise in Crohn's disease (CD), however it is encumbered by the risk of progressive multifocal leukoencephalopathy. Vedolizumab inhibits the α4β7 integrin to induce clinical remission in patients with both ulcerative colitis and CD. Long-term safety data on this agent is not yet available. We also review agents in the pipeline. Finally, we discuss the positioning of therapies and potential alterations to therapeutic algorithms as new medications emerge. CONCLUSIONS: New therapies are emerging for IBD; however, long-term data are pending. The positioning of these agents in algorithms will evolve.

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 score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.283
Teacher spread0.270 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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