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Record W3211769902 · doi:10.1016/j.ynirp.2021.100064

Brain structure and function changes in ulcerative colitis

2021· article· en· W3211769902 on OpenAlexafffund
Jennifer Kornelsen, Kelcie Witges, Jennifer S. Labus, Emeran A. Mayer, Çharles N. Bernstein

Bibliographic record

VenueNeuroimage Reports · 2021
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsUniversity of Manitoba
FundersUniversity of California, Los AngelesNational Institutes of HealthNational Institute of Diabetes and Digestive and Kidney DiseasesUniversity of Manitoba
KeywordsUlcerative colitisVoxel-based morphometryGrey matterMagnetic resonance imagingBrain Structure and FunctionVoxelDorsumFunctional magnetic resonance imagingBrain morphometryNeuroscienceDefault mode networkInflammatory bowel diseaseMedicinePathologyPsychologyDiseaseWhite matterNeuroimagingAnatomyRadiology

Abstract

fetched live from OpenAlex

As the importance of the brain-gut axis in the pathobiology of inflammatory bowel disease continues to evolve, a greater understanding of brain structure and brain functional connectivity (FC) in diseases such as ulcerative colitis (UC) are necessary. In this magnetic resonance imaging (MRI) study, we investigated differences in brain structure and in FC of brain regions in 76 participants with UC and 74 healthy controls (HC). Voxel based morphometry analysis indicated greater grey matter volume in multiple brain regions in UC as compared to HC. Differences in FC between groups were identified in the cerebellar, default mode, visual, and dorsal attention networks. FC differed by sex for the visual and dorsal attention networks. These differences provide evidence that the brain-gut axis is altered in UC and warrant further investigation to determine if they help direct the evolution of UC or if they evolve in response to the presence of UC.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.264
Teacher spread0.236 · 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 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

Citations17
Published2021
Admission routes2
Has abstractyes

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