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Record W2990346698 · doi:10.14218/jcth.2019.00028

Pathogenesis of Insulin Resistance and Atherogenic Dyslipidemia in Nonalcoholic Fatty Liver Disease

2019· review· en· W2990346698 on OpenAlexaff
Daud Akhtar, Umair Iqbal, Luis Miguel Vazquez‐Montesino, Brittany B. Dennis, Aijaz Ahmed

Bibliographic record

VenueJournal of Clinical and Translational Hepatology · 2019
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsMcMaster UniversityUniversity of British Columbia
Fundersnot available
KeywordsDyslipidemiaInsulin resistanceMedicineNonalcoholic fatty liver diseaseInternal medicineFatty liverPathogenesisMetabolic syndromeDiseaseLipid metabolismObesityEndocrinologyGastroenterology

Abstract

fetched live from OpenAlex

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in the developed world, with a global prevalence of around 25%. NAFLD is considered to be the hepatic manifestation of metabolic syndrome and is strongly associated with obesity, insulin resistance and dyslipidemia. Insulin resistance plays a pivotal role in the development of NAFLD-related dyslipidemia, which ultimately increases the risk of premature cardiovascular diseases, a leading cause of morbidity and mortality in patients with NAFLD. Insulin affects hepatic glucose and lipid metabolism by hepatic or extrahepatic pathways. Aside from insulin resistance, several other factors also contribute to the pathogenesis of atherogenic dyslipidemia in patients with NAFLD. These include diet composition, gut microbiota and genetic factors, to name a few. The identification of potentially modifiable risk factors of NAFLD is of importance, so as to target those who may benefit from lifestyle changes and to help develop targeted therapies that decrease the risk of cardiovascular diseases in patients with 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 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)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.152
GPT teacher head0.429
Teacher spread0.277 · 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

Citations84
Published2019
Admission routes1
Has abstractyes

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