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Record W3012319749

Gut microbiome: pertinence in fibromyalgia.

2020· article· en· W3012319749 on OpenAlexaff
Amir Minerbi, Mary‐Ann Fitzcharles

Bibliographic record

VenuePubMed · 2020
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMicrobiomeFibromyalgiaMedicineGut floraDiseaseIrritable bowel syndromeMoodPathogenesisGut microbiomeMood disordersHost (biology)Chronic painImmunologyBioinformaticsAnxietyBiologyPsychiatryInternal medicineEcology
DOInot available

Abstract

fetched live from OpenAlex

The human gut microbiome constitutes a diverse and dynamic community of microorganisms that inhabit the digestive tract. In recent years, there is growing appreciation for the role of the gut microbiome in host health and disease. Gut bacteria are involved in the pathogenesis of numerous medical conditions in a variety of medical fields including gastroenterology, metabolic, rheumatologic, neurologic and psychiatric disorders. Recently, evidence is mounting that gut bacteria could also play a role in chronic pain and specifically fibromyalgia (FM). The composition of the gut bacterial community is altered in individuals with FM, with an altered abundance of a small subset of bacterial species. Some of these species, either with increased or decreased abundance in patients, have established metabolic activity which could have pertinence in the expression of FM symptoms. The putative mechanisms which could allow these bacterial species to affect pain, fatigue, mood and other symptoms include the entry of short-chain-fatty-acids, bile acids, neurotransmitters and bacterial antigens into the host circulation. While these are merely the first steps in understanding the role of the gut microbiome in chronic pain and specifically FM, one might envision exciting future perspectives for better mechanistic understanding of FM, for the development of objective diagnostic aids and potentially for new therapeutic modalities.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.268

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 head0.034
GPT teacher head0.230
Teacher spread0.196 · 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 designObservational
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

Citations45
Published2020
Admission routes1
Has abstractyes

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