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Record W3047682179 · doi:10.1080/07853890.2020.1808239

Gut microbes in neurocognitive and mental health disorders

2020· review· en· W3047682179 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnals of Medicine · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNeurocognitiveDysbiosisGut floraMental healthDementiaSchizophrenia (object-oriented programming)AnxietyMedicineBipolar disorderPsychiatryDepression (economics)MicrobiomePsychologyCognitionBioinformaticsDiseaseImmunologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Scoping review about the effect of gut microbiota on neurocognitive and mental health disorders. RESULTS: This scoping review found there is an evolving evidence of the involvement of the gut microbiota in the pathophysiology of neurocognitive and mental health disorders. This manuscript also discusses how the psychotropics used to treat these conditions may have an antimicrobial effect on GM, and the potential for new strategies of management with probiotics and faecal transplantation. CONCLUSIONS: This understanding can open up the need for a gut related approach in these disorders as well as unlock the door for the role of gut related microbiota management. KEY MESSAGES Challenges of managing mental health conditions remain in spite of new pharmacological therapy. Gut dysbiosis is seen in various mental health conditions. Various psychotropic medications can have an influence on the gut microbiota by their antimicrobial effect.

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.

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: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.059
GPT teacher head0.410
Teacher spread0.351 · 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