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A Prospective Evaluation of Transient Elastography in Non-Alcoholic Fatty Liver Disease - BMI and Subcutaneous Fat Thickness Do Not Affect Accuracy

2012· article· en· W2977367706 on OpenAlexaffabout
David Yik, Melanie Beaton, Natasha Chandok

Bibliographic record

VenueThe American Journal of Gastroenterology · 2012
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineTransient elastographySteatosisLiver biopsySteatohepatitisFatty liverMagnetic resonance imagingNonalcoholic fatty liver diseaseMagnetic resonance elastographyBody mass indexInternal medicineHepatic fibrosisProspective cohort studyBiopsyRadiologyGastroenterologyFibrosisElastographyUltrasoundDisease

Abstract

fetched live from OpenAlex

Purpose: Transient elastography (TE) has emerged as an important non-invasive technique of evaluating hepatic fibrosis. However, recent data suggest that greater hepatic steatosis may downgrade TE values relative to the degree of hepatic fibrosis on biopsy in nonalcoholic fatty liver disease (NAFLD) patients. This prospective study evaluates the relationship between TE values and hepatic steatosis as measured on magnetic resonance imaging (MRI) of the liver. Other variables which may affect TE accuracy in NAFLD were also investigated, including higher patient body mass index (BMI) and greater subcutaneous fat overlying the liver. Methods: All adult patients at a tertiary care centre in London, Canada, who met diagnostic criteria for NAFLD were invited to participate in this study. All patients underwent liver biopsy, TE and had anthropomorphic measurements, liver transaminases and measures of metabolic dysfunction measured. Liver biopsies were read by a single, blinded pathologist and scored according to the validated nonalcoholic steatohepatitis activity score (NAS). The FibroScan 502 system (Echosens) was used to conduct TE measurements for all study patients. Subcutaneous fat and hepatic steatosis measured as hepatic fat fraction was measured using Magnetic Resonance Imaging with Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL-MRI), a computer-based quantification method separating fat and water signals in MRI images allowing accurate, validated and reproducible measurement of subcutaneous fat thickness. Linear regression analysis was used to evaluate the correlation between BMI and TE values; subcutaneous fat thickness and TE values; and degree of fibrosis on liver biopsy and TE values. Results: Linear regression demonstrated a positive correlation between TE values and the degree of fibrosis on liver biopsy (P<0.001). There was no correlation between subcutaneous fat thickness and TE values (P=0.699), between BMI and TE values (P=0.883), between subcutaneous fat thickness and liver fibrosis score (p=0.183), nor between BMI and liver fibrosis score (p=0.057). There was no correlation between hepatic fat fraction and TE values (p=0.152), nor hepatic fat fraction and liver fibrosis score (p=0.0118). Conclusion: This study demonstrates that TE correlates well with the degree of hepatic fibrosis on liver biopsy in NAFLD patients, and suggests that common anthropomorphic features of such patients such as increased hepatic steatosis, increased BMI, and peri-hepatic subcutaneous fat does not adversely affect the reliability of TE. This finding supports the continued use of this important diagnostic modality in a population that is becoming increasingly prevalent.

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)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.017
GPT teacher head0.297
Teacher spread0.279 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2012
Admission routes2
Has abstractyes

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