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Record W4400958298 · doi:10.1080/14712598.2024.2383882

The potential for medical therapies to address fistulizing Crohn’s disease: a state-of-the-art review

2024· review· en· W4400958298 on OpenAlexaff
Mohammad Shehab, Davide De Marco, Péter L. Lakatos, Talat Bessissow

Bibliographic record

VenueExpert Opinion on Biological Therapy · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineCrohn's diseaseDiseaseIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Crohn's disease (CD) is a chronic, relapsing immune mediated disease, which is one of the two major types of inflammatory bowel disease (IBD). Fistulizing CD poses a significant clinical challenge for physicians. Effective management of CD requires a multidisciplinary approach, involving a gastroenterologist and a GI surgeon while tailoring treatment to each patient's unique risk factors, clinical representations, and preferences. AREAS COVERED: This comprehensive review explores the intricacies of fistulizing CD including its manifestations, types, impact on quality of life, management strategies, and novel therapies under investigation. EXPERT OPINION: Antibiotics are often used as first-line therapy to treat symptoms. Biologics that selectively target TNF-α, such infliximab (IFX), have shown high efficacy in randomized controlled trials. However, more than 50% of patients lose response to IFX, prompting them to explore alternative strategies. Current options include adalimumab and certolizumab pegol combination therapies, as well as small-molecule drugs targeting Janus kinases such as Upadacitinib. Furthermore, a promising treatment for complex fistulas is mesenchymal stem cells such as Darvadstrocel (Alofisel), an allogeneic stem cell-based therapy. However, surgical interventions are necessary for complex cases or intra-abdominal complications. Setons and LIFT procedures are the most common surgical options.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.899
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.055
GPT teacher head0.384
Teacher spread0.329 · 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.

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

Citations6
Published2024
Admission routes1
Has abstractyes

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