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Record W4405547059 · doi:10.5772/intechopen.1008363

Principles of Nutrition in Patients with Non-alcoholic Fatty Liver Disease

2024· book-chapter· en· W4405547059 on OpenAlexfundno aff
Narges Mobasheri, Leila Ghahremani, Mahin Nazari

Bibliographic record

VenueIntechOpen eBooks · 2024
Typebook-chapter
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsnot available
FundersHealth Canada
KeywordsNonalcoholic fatty liver diseaseMedicineMediterranean dietDashDietary fiberDiseaseDASH dietIntensive care medicineLiver diseaseWeight lossFatty liverEnvironmental healthInternal medicineObesityFood scienceBiologyComputer science

Abstract

fetched live from OpenAlex

This chapter will comprehensively address the nutritional principles essential for managing nonalcoholic fatty liver disease (NAFLD). It will explore the critical role of diet in the prevention and treatment of NAFLD, providing evidence-based dietary recommendations to improve liver health and the overall well-being of patients. Key topics will include the importance of balanced macronutrient intake, the role of specific nutrients and food groups (e.g., antioxidants, fiber, and healthy fats), and the impact of dietary patterns such as the Mediterranean and Dietary Approaches to Stop Hypertension (DASH) diet on liver fat reduction and inflammation. Additionally, the chapter will discuss the significance of weight management, outlining the standard amount of weight loss beneficial for NAFLD patients. Practical guidelines on how to implement these dietary changes, overcome common barriers, and maintain long-term adherence will be provided. This chapter aims to equip healthcare professionals and patients with the knowledge and tools necessary to effectively manage NAFLD through diet, ultimately improving patient outcomes and quality of life.

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: Other · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
GPT teacher head0.238
Teacher spread0.220 · 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
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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