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Record W2513684287 · doi:10.3748/wjg.v22.i34.7727

Co-existence of non-alcoholic fatty liver disease and inflammatory bowel disease: A review article

2016· review· en· W2513684287 on OpenAlexaff
Che‐Yung Chao, Robert Battat, Alex Al Khoury, Sophie Restellini, Giada Sebastiani, Talat Bessissow

Bibliographic record

VenueWorld Journal of Gastroenterology · 2016
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsMcGill University Health Centre
FundersGilead Sciences
KeywordsMedicineFatty liverDiseaseInflammatory bowel diseaseObservational studyAlcoholic liver diseaseSteatosisInsulin resistanceDysbiosisLiver diseaseInternal medicineIntestinal permeabilityIntensive care medicineObesityGastroenterologyBioinformaticsCirrhosisBiology

Abstract

fetched live from OpenAlex

Emerging data have highlighted the co-existence of non-alcoholic fatty liver disease (NAFLD) and inflammatory bowel disease; both of which are increasingly prevalent disorders with significant complications and impact on future health burden. Cross-section observational studies have shown widely variable prevalence rates of co-existing disease, largely due to differences in disease definition and diagnostic tools utilised in the studies. Age, obesity, insulin resistance and other metabolic conditions are common risks factors in observational studies. However, other studies have also suggested a more dominant role of inflammatory bowel disease related factors such as disease activity, duration, steroid use and prior surgical intervention, in the development of NAFLD. This suggests a potentially more complex pathogenesis and relationship between the two diseases which may be contributed by factors including altered intestinal permeability, gut dysbiosis and chronic inflammatory response. Commonly used immunomodulation agents pose potential hepatic toxicity, however no definitive evidence exist linking them to the development of hepatic steatosis, nor are there any data on the impact of therapy and prognosis in patient with co-existent diseases. Further studies are required to assess the impact and establish appropriate screening and management strategies in order to allow early identification, intervention and improve patient 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.286
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.024
GPT teacher head0.320
Teacher spread0.296 · 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 designObservational
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

Citations86
Published2016
Admission routes1
Has abstractyes

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