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Record W2912747521 · doi:10.3389/fendo.2019.00081

Disentangling What We Know About Microbes and Mental Health

2019· article· en· W2912747521 on OpenAlexaff
Gregor Reid

Bibliographic record

VenueFrontiers in Endocrinology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsWestern UniversityLawson Health Research Institute
Fundersnot available
KeywordsMental healthMicrobiomeGut floraGut–brain axisHappinessGut microbiomeDiseasePerspective (graphical)AnxietyReductionismPsychologyNeuroscienceEnvironmental ethicsEcologyBiologyMedicinePsychiatryBioinformaticsImmunologySocial psychology

Abstract

fetched live from OpenAlex

Much has been written in recent years about the gut-brain axis. Exciting pilot studies suggest probiotic applications to the gut can reduce anxiety and depression via the vagus nerve. But not to diminish such findings, much still needs to be considered, including the fact that the vagus nerve links to many other body sites that also host a microbiome. Questions remain that touch the core of being human: (i) Do our microbes influence happiness and to what extent? (ii) What components of the gut microbiota and their function, including as it relates to mental health, are critical and how do they differ between agile, fit hunter gatherers and obese westerners or Danes described as the happiest people on the planet? (iii) What role do environmental pollutants play in this microbes-host ecosystem? While approaching life from a reductionist perspective has a long history in science, we need to try to interrogate these health and disease issues from a wider perspective. For verification of a link between the gut microbiota and brain, and to test new therapies, human studies are needed, and are long overdue.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.004
GPT teacher head0.243
Teacher spread0.239 · 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
GenreEmpirical

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

Citations21
Published2019
Admission routes1
Has abstractyes

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