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Record W2018677712 · doi:10.2147/jir.s65979

Inflammatory bowel disease: clinical aspects and treatments

2014· review· en· W2018677712 on OpenAlexaff
Marc Fakhoury, Hani Al‐Salami, Rebecca Negrulj, Armin Mooranian

Bibliographic record

VenueJournal of Inflammation Research · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsMcGill University
Fundersnot available
KeywordsUlcerative colitisMedicineInflammatory bowel diseaseDiseaseInflammationImmune systemAbdominal painPathophysiologyDiarrheaBloody diarrheaImmunologyInflammatory Bowel DiseasesCrohn's diseaseGastroenterologyColitisInternal medicine

Abstract

fetched live from OpenAlex

Inflammatory bowel disease (IBD) is defined as a chronic intestinal inflammation that results from host-microbial interactions in a genetically susceptible individual. IBDs are a group of autoimmune diseases that are characterized by inflammation of both the small and large intestine, in which elements of the digestive system are attacked by the body's own immune system. This inflammatory condition encompasses two major forms, known as Crohn's disease and ulcerative colitis. Patients affected by these diseases experience abdominal symptoms, including diarrhea, abdominal pain, bloody stools, and vomiting. Moreover, defects in intestinal epithelial barrier function have been observed in a number of patients affected by IBD. In this review, we first describe the types and symptoms of IBD and investigate the role that the epithelial barrier plays in the pathophysiology of IBD as well as the major cytokines involved. We then discuss steps used to diagnose this disease and the treatment options available, and finally provide an overview of the recent research that aims to develop new therapies for such chronic disorders.

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 score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.003

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 head0.074
GPT teacher head0.444
Teacher spread0.370 · 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

Citations519
Published2014
Admission routes1
Has abstractyes

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