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Record W2611411467 · doi:10.3390/nu9050447

The Impact of Exclusive Enteral Nutrition (EEN) on the Gut Microbiome in Crohn’s Disease: A Review

2017· review· en· W2611411467 on OpenAlexafffund
Amber L. MacLellan, Jessica Connors, Shannan Grant, Leah E. Cahill, Morgan G. I. Langille, Johan Van Limbergen

Bibliographic record

VenueNutrients · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsMount Saint Vincent UniversityDalhousie UniversityIzaak Walton Killam Health Centre
FundersCanadian Institutes of Health ResearchIWK Health CentreNorth American Society for Pediatric Gastroenterology, Hepatology and NutritionDalhousie UniversityNova Scotia Health Research FoundationDalhousie Medical Research FoundationCrohn's and Colitis Foundation of America
KeywordsMicrobiomeMedicineCrohn's diseaseGut microbiomeDiseaseEnteral administrationIntestinal MicrobiomeParenteral nutritionInflammatory bowel diseaseCrohn diseaseGastroenterologyIntensive care medicineInternal medicineBiologyBioinformatics

Abstract

fetched live from OpenAlex

Crohn's disease (CD), a form of inflammatory bowel disease (IBD), is thought to arise from a complex interaction of genetics, the gut microbiome, and environmental factors, such as diet. There is clear evidence that dietary intervention is successful in the treatment of CD-exclusive enteral nutrition (EEN) is able to induce remission in up to 80% of CD patients. While the mechanism of action of EEN is not clear, EEN is known to cause profound changes in the gut microbiome. Understanding how EEN modifies the gut microbiome to induce remission could provide insight into CD etiopathogenesis and aid the development of microbiome-targeted interventions to guide ongoing dietary therapy to sustain remission. This review includes current literature on changes in composition and function of the gut microbiome associated with EEN treatment in CD patients.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.037
GPT teacher head0.371
Teacher spread0.334 · 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 designSystematic review
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

Citations118
Published2017
Admission routes2
Has abstractyes

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