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Record W4417296346 · doi:10.1111/imj.70263

Hepatic steatosis detected on low‐dose computed tomography in high‐risk participants of a lung cancer screening programme: a retrospective observational study

2025· article· en· W4417296346 on OpenAlexaff
Asha Bonney, Michelle Chua, Diane Pascoe, Mark McCusker, Daniel Steinfort, Siddharth Sood, Henry Marshall, Fraser Brims, Annette McWilliams, Emily Stone, Paul Fogarty, Kwun M. Fong, Stephen Lam, Renée Manser

Bibliographic record

VenueInternal Medicine Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of British Columbia
FundersMedical Research Future FundNational Health and Medical Research CouncilRoyal Melbourne Hospital
KeywordsSteatosisComputed tomographyRetrospective cohort studyObservational studyLung cancerLung cancer screeningLung

Abstract

fetched live from OpenAlex

Moderate to severe hepatic steatosis was detected on low-dose computed tomography (LDCT) in 15% of participants undergoing lung cancer screening in a retrospective observational study. Almost half of participants with hepatic steatosis (21/45) were at intermediate risk of liver fibrosis, with four participants (8.9%) at high risk based on Fibrosis-4 scores. Based on current guidelines, these participants would be recommended to undergo further evaluation for liver disease. The clinical impact of moderate to severe hepatic steatosis incidentally detected on lung cancer screening LDCT requires further consideration and evaluation.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.014
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.358
Teacher spread0.311 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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