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Record W7117915229 · doi:10.1016/j.ejrad.2025.112637

Evaluation of two software programs for cross‐sectional body composition analysis on abdominal computed tomography scans of patients with cirrhosis

2025· article· en· W7117915229 on OpenAlexaffabout
Francesca D’Arcangelo, Alberto Zanetto, Abha Dunichand-Hoedl, Maryam Motamedrad, Patrizia Burra, Montano-Loza Aldo

Bibliographic record

VenueEuropean Journal of Radiology · 2025
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Alberta Hospital
Fundersnot available
KeywordsAdipose tissueCirrhosisComputed tomographyAbdominal fatAbdomenSarcopeniaSubcutaneous fatSubcutaneous adipose tissueSoft tissue

Abstract

fetched live from OpenAlex

The impact of body composition abnormalities and clinical outcomes in patients with cirrhosis is well established. Abdominal computed tomography (CT) evaluation is the technique that is better evaluated; however, agreements are not well known. Fifty patients were randomly selected from two centers, and two observers independently evaluated their CT. The cross-sectional muscle area (CSMA), skeletal muscle index (SMI), muscle attenuation (MA), visceral adipose tissue area (VAT), visceral adipose tissue index (VATI), subcutaneous adipose tissue area (SAT), and subcutaneous adipose tissue area (SATI) were analyzed using SliceOmatic©V.5.0 (Magog, Canada), and Synapse 3D, Fujifilm. The interobserver and intersoftware intra-class correlation coefficients (ICCs) were highly equivalent for CSMA, SMI, MA, VAT, VATI, SAT, and SATI (Range 0.984-0.999, P < 0.001), and excellent Pearson's correlation coefficients were found for all comparisons (Range 0.970-0.998). Using different software programs to evaluate body composition in patients with cirrhosis showed excellent agreement for measuring muscle mass and adipose depots.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.035
GPT teacher head0.354
Teacher spread0.319 · 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
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

Citations1
Published2025
Admission routes2
Has abstractyes

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