MétaCan
Menu
Back to cohort
Record W2736129232 · doi:10.3920/978-90-8686-839-1_8

Chapter 8 The interplay between the microbiota and the central nervous system during neurodevelopment

2017· book-chapter· en· W2736129232 on OpenAlexaff
Aadil Bharwani, John Bienenstock, Paul Forsythe

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldNeuroscience
TopicVagus Nerve Stimulation Research
Canadian institutionsHealth Sciences CentreMcMaster University Medical CentreMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsNeuroscienceGut–brain axisCentral nervous systemGut floraBiologyImmune systemSignallingBrain functionImmunology

Abstract

fetched live from OpenAlex

Recent advances in technology and research have led to tremendous strides in understanding the critical role of gut bacteria in neurodevelopment. Both the presence and composition of this intestinal community influences various aspects of central and enteric nervous systems physiology, thus shaping behaviour and neural function. Furthermore, signalling along the gut-brain axis, even through a single bacterial species, can alter the developmental trajectory of the stress circuitry and functional responses to stress. Gut-brain signalling is complex and bidirectional, mediated through multiple candidate pathways that enable this interplay, including the vagus nerve, the immune system, and an array of metabolite mediators. Given the sensitivity of the early developmental period to environmental perturbations, and the possible long-term consequences on the onset of neurodevelopmental and psychiatric conditions, further understanding of the mechanisms underlying gut-brain signalling and its role in development is critical to gaining insight into such conditions and identifying potentially novel therapeutic strategies.

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)0.000
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.030
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0300.013

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 head0.041
GPT teacher head0.289
Teacher spread0.249 · 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
GenreOther

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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicVagus Nerve Stimulation ResearchFrench-language works237,207