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Probiotics in the Treatment of Inflammatory Bowel Disease

2006· review· en· W1982326403 on OpenAlexaff
Kevin P. Rioux, Richard N. Fedorak

Bibliographic record

VenueJournal of Clinical Gastroenterology · 2006
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsCanadian Institutes of Health ResearchUniversity of Alberta
Fundersnot available
KeywordsSynbioticsPouchitisMedicineInflammatory bowel diseaseProbioticUlcerative colitisAntimicrobialAntibioticsImmune systemDiseaseCrohn's diseaseImmunologyClinical trialIntensive care medicineMicrobiologyInternal medicineBacteriaBiology

Abstract

fetched live from OpenAlex

The demonstration that immune and epithelial cells can discriminate between different microbial species has extended our understanding of the actions of probiotics beyond simple antimicrobial concepts. Several probiotic mechanisms of action, relative to inflammatory bowel disease, have been elucidated: (1) competitive exclusion, whereby probiotics compete with microbial pathogens; (2) immunomodulation and/or stimulation of an immune response; (3) antimicrobial activity and suppression of pathogen growth; (4) enhancement of barrier activity; and (5) induction of T cell apoptosis. The unraveling of these mechanisms of action has led to new support for the use of probiotics in the management of clinical inflammatory bowel disease. While level 1 evidence now supports the therapeutic use of some probiotics in the maintenance treatment of pouchitis, only level 2 and 3 evidence are currently available in support of the use of probiotics in the treatment of ulcerative colitis and Crohn's disease. Nevertheless, one significant and consistent finding has emerged over the course of research in the past year: not all probiotic bacteria have similar therapeutic effects. Rigorously designed, controlled clinical trials, to investigate the unresolved issues related to efficacy, dose, duration of use, single or multistrain formulation, and the concomitant use of prebiotics, synbiotics or antibiotics, are vital.

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)0.000
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 score0.902
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
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 head0.040
GPT teacher head0.372
Teacher spread0.332 · 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

Citations92
Published2006
Admission routes1
Has abstractyes

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