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Record W4317438898 · doi:10.1136/gutjnl-2020-320633

Understanding neuroimmune interactions in disorders of gut–brain interaction: from functional to immune-mediated disorders

2023· review· en· W4317438898 on OpenAlexafffund
Tim Vanuytsel, Přemysl Berčík, Guy E. Boeckxstaens

Bibliographic record

VenueGut · 2023
Typereview
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
FundersCanadian Institutes of Health ResearchVlaamse regeringKU LeuvenFonds Wetenschappelijk Onderzoek
KeywordsIrritable bowel syndromeImmune systemPathophysiologyMedicineInflammationFunctional gastrointestinal disorderNeuroscienceImmunologyBioinformaticsBiologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Functional gastrointestinal disorders-recently renamed into disorders of gut-brain interaction-such as irritable bowel syndrome and functional dyspepsia are highly prevalent conditions with bothersome abdominal symptoms in the absence of structural abnormalities. While traditionally considered as motility disorders or even psychosomatic conditions, our understanding of the pathophysiology has evolved significantly over the last two decades. Initial observations of subtle mucosal infiltration with immune cells, especially mast cells and eosinophils, are since recently being backed up by mechanistic evidence demonstrating increased release of nociceptive mediators by immune cells and the intestinal epithelium. These mediators can activate sensitised neurons leading to visceral hypersensitivity with bothersome symptoms. The interaction between immune activation and an impaired barrier function of the gut is most likely a bidirectional one with alterations in the microbiota, psychological stress and food components as upstream players in the pathophysiology. Only few immune-targeting treatments are currently available, but an improved understanding through a multidisciplinary scientific approach will hopefully identify novel, more precise treatment targets with ultimately better outcomes.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.886
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.246
GPT teacher head0.380
Teacher spread0.134 · 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.

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

Citations130
Published2023
Admission routes2
Has abstractyes

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