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Record W2079150934 · doi:10.4161/gmic.29417

The microbiota-gut-brain axis in functional gastrointestinal disorders

2014· review· en· W2079150934 on OpenAlexafffund
Giada De Palma, Stephen M. Collins, Přemysl Berčík

Bibliographic record

VenueGut Microbes · 2014
Typereview
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsIrritable bowel syndromeGut–brain axisGut floraFunctional gastrointestinal disorderBiologyPathophysiologyBioinformaticsMedicineInternal medicineImmunology

Abstract

fetched live from OpenAlex

Functional gastrointestinal disorders (FGIDs) are highly prevalent and pose a significant burden on health care and society, and impact patients' quality of life. FGIDs comprise a heterogeneous group of disorders, with unclear underlying pathophysiology. They are considered to result from the interaction of altered gut physiology and psychological factors via the gut-brain axis, where brain and gut symptoms are reciprocally influencing each other's expression. Intestinal microbiota, as a part of the gut-brain axis, plays a central role in FGIDs. Patients with Irritable Bowel Syndrome, a prototype of FGIDs, display altered composition of the gut microbiota compared with healthy controls and benefit, at the gastrointestinal and psychological levels, from the use of probiotics and antibiotics. This review aims to recapitulate the available literature on FGIDs and microbiota-gut-brain axis.

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)0.000
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
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 head0.026
GPT teacher head0.296
Teacher spread0.270 · 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

Citations157
Published2014
Admission routes2
Has abstractyes

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