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Record W2054192772 · doi:10.1097/mcg.0b013e31824cf51e

The Role of Nutrients in the Development, Progression, and Treatment of Nonalcoholic Fatty Liver Disease

2012· review· en· W2054192772 on OpenAlexaff
Marialena Mouzaki, Johane P. Allard

Bibliographic record

VenueJournal of Clinical Gastroenterology · 2012
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of TorontoUniversity Health NetworkHospital for Sick ChildrenSickKids Foundation
Fundersnot available
KeywordsNonalcoholic fatty liver diseaseMedicinePolyunsaturated fatty acidMicronutrientSteatosisFatty liverInternal medicinePhysiologyEndocrinologyDiseaseFatty acidBiologyBiochemistryPathology

Abstract

fetched live from OpenAlex

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of liver disease in adults and children and is currently the third most common indication for liver transplantation in North America. Its pathogenesis is thought to be secondary to multiple "hits" derived from the dietary components, adipose tissue, immune system, and intestinal microbiota. Lack of physical activity may contribute as well. Nutrients may exert their effect directly or through alteration of the intestinal microbiota. Research focusing on specific dietary components predisposing to NAFLD has shown conflicting results. Total energy intake, and macronutrients, has been linked to the development of NAFLD. Fructose not only contributes to hepatic steatosis but may trigger inflammatory signals as well. Polyunsaturated fatty acids are thought to exert anti-inflammatory effects. The role of vitamins as well as minerals in this field is actively being investigated. In this review, we discuss the evidence-linking macronutrients (such as carbohydrates and fat in general and fructose, fiber, short chain fatty acids, polyunsaturated fatty, and choline specifically) and micronutrients (such as vitamin E and C and minerals) with the development and treatment of NAFLD. We also discuss the literature on physical activity and NAFLD.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.509
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.119
GPT teacher head0.438
Teacher spread0.319 · 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
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

Citations117
Published2012
Admission routes1
Has abstractyes

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