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Record W2565583047 · doi:10.1177/1352458516682105

Breaking down the gut microbiome composition in multiple sclerosis

2016· review· en· W2565583047 on OpenAlexaff
Adrian Budhram, Seema Nair Parvathy, Marcelo Kremenchutzky, Michael Silverman

Bibliographic record

VenueMultiple Sclerosis Journal · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsSt Joseph's Health CareUniversity Hospital
Fundersnot available
KeywordsMultiple sclerosisGut microbiomeMicrobiomeMedicineBiologyNeuroscienceComputational biologyImmunologyBioinformatics

Abstract

fetched live from OpenAlex

BACKGROUND: The gut microbiome, which consists of a highly diverse ecologic community of micro-organisms, has increasingly been studied regarding its role in multiple sclerosis (MS) immunopathogenesis. This review critically examines the literature investigating the gut microbiome in MS. METHODS: A comprehensive search was performed of PubMed databases and ECTRIMS meeting abstracts for literature relating to the gut microbiome in MS. Controlled studies examining the gut microbiome in patients with MS were included for review. RESULTS: Identified studies were predominantly case-control in their design and consistently found differences in the gut microbiome of MS patients compared to controls. We examine plausible mechanistic links between these differences and MS immunopathogenesis, and discuss the therapeutic implications of these findings. CONCLUSIONS: Review of the available literature reveals potential immunopathogenic links between the gut microbiome and MS, identifies avenues for therapeutic advancement, and emphasizes the need for further systematic study in this emerging field.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.106
GPT teacher head0.300
Teacher spread0.194 · 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 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

Citations50
Published2016
Admission routes1
Has abstractyes

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