MétaCan
Menu
Back to cohort
Record W645780470 · doi:10.1155/2015/273595

Nonmedicinal Interventions in Nonalcoholic Fatty Liver Disease

2015· review· en· W645780470 on OpenAlexaff
Manuela G. Neuman, Radu M. Nanau, Lawrence B. Cohen

Bibliographic record

VenueCanadian Journal of Gastroenterology and Hepatology · 2015
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineNonalcoholic fatty liver diseaseSteatosisFibrosisDiseaseFatty liverInternal medicineLiver diseaseSteatohepatitisGastroenterologyNonalcoholic steatohepatitisBioinformatics

Abstract

fetched live from OpenAlex

Unhealthy diet and lack of physical exercise are responsible for fat accumulation in the liver, which may lead to liver disease. Histologically, the severity of the disease has two stages: nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH). NAFLD is defined by the presence of steatosis with no evidence of cellular injury such as hepatocyte ballooning. NASH is a distinct entity from NAFLD, and is characterized by the presence of inflammation with hepatocytes damage, with or without fibrosis. While several therapeutic strategies have been proposed to improve this condition, the present review aims to discuss nonmedicinal interventions used to reduce liver involvement or to prevent the disease altogether. The authors investigated dietary patterns and vitamin deficiencies associated with NAFLD, and their role in enhancing disease severity. Additionally, they reviewed the role of exercise and the use of interventions, such as as intragastric balloon and bariatric surgery, for improving disease progression. The authors propose monitoring disease progression or repair by following changes in cytoadipokine levels.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.102
GPT teacher head0.353
Teacher spread0.251 · 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 designNot applicable
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

Citations13
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Gastroenterology and HepatologySame topicLiver Disease Diagnosis and TreatmentFrench-language works237,207