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Record W2736566316 · doi:10.3390/nu9080800

Dietary Composition Independent of Weight Loss in the Management of Non-Alcoholic Fatty Liver Disease

2017· review· en· W2736566316 on OpenAlexaff
Tannaz Eslamparast, Puneeta Tandon, Maitreyi Raman

Bibliographic record

VenueNutrients · 2017
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsWeight lossFatty liverMedicineSteatosisMediterranean dietOverconsumptionInternal medicineMetabolic syndromeComposition (language)Weight managementDiseasePhysiologyGastroenterologyObesity

Abstract

fetched live from OpenAlex

Poor dietary composition is an important factor in the progression of non-alcoholic fatty liver disease (NAFLD). The majority of NAFLD patients follow diets with overconsumption of simple carbohydrates, total and saturated fat, with reduced intake of dietary fiber and omega-3 rich foods. Although lifestyle modifications including weight loss and exercise remain the keystone of NAFLD management, modifying dietary composition with or without a calorie-restricted diet may also be a feasible and sustainable strategy for NAFLD treatment. In the present review article, we highlight the potential therapeutic role of a "high quality healthy diet" to improve hepatic steatosis and metabolic dysfunction in patients with NAFLD, independent of caloric restriction and weight loss. We provide a literature review evaluating the evidence behind dietary components including fiber-, meat- and omega-3-rich diets and, pending further evidence, we concur with the EASL-EASD-EASO Clinical Guidelines recommendation of the Mediterranean diet as the diet of choice in these patients.

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: Review · Consensus signal: Review
Teacher disagreement score0.811
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.074
GPT teacher head0.363
Teacher spread0.290 · 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

Citations105
Published2017
Admission routes1
Has abstractyes

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