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Record W3198455939 · doi:10.1097/mcg.0000000000001555

Management of Cardiometabolic Complications in Patients With Nonalcoholic Fatty Liver Disease

2021· review· en· W3198455939 on OpenAlexaff
Brittany B. Dennis, Sandy Sallam, Brandon J. Perumpail, Neha D. Shah, Donghee Kim, George Cholankeril, Aijaz Ahmed

Bibliographic record

VenueJournal of Clinical Gastroenterology · 2021
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNonalcoholic fatty liver diseaseMedicineSteatosisMetabolic syndromeFatty liverDyslipidemiaInternal medicineInsulin resistanceSteatohepatitisFibrosisLiver diseaseGastroenterologyDiseaseObesity

Abstract

fetched live from OpenAlex

Nonalcoholic fatty liver disease (NAFLD) comprises a spectrum of liver conditions characterized by significant lipid deposition within hepatocytes. As an overarching diagnosis, NAFLD contains a continuum of progressive liver diseases ranging from isolated liver steatosis to necroinflammatory states leading to end-stage liver disease. Nonalcoholic fatty liver and nonalcoholic steatohepatitis are distinguished by their histologic patterns, with the former exhibiting steatosis without fibrosis or inflammation. This important distinction provides clinicians a timeline within the NAFLD staging to target appropriate interventions against modifiable risk factors. NAFLD is likely formed in response to metabolic imbalances that damage the livers adaptive capacity. Metabolic conditions leading to steatosis mirror common cardiovascular risk factors, including dyslipidemia, diabetes mellitus, and obesity. Acknowledging the common risk factors for development and progression of NAFLD, it is unsurprising the first-line management focuses on the treatment of metabolic syndrome with an emphasis on weight reduction in obese populations. The purpose of this review is to provide a detailed summary of the literature as well as outline the current treatment recommendations for patients with NAFLD with a detailed focus on pharmacologic antiobesity interventions.

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: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.254
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
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 head0.080
GPT teacher head0.409
Teacher spread0.329 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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