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Record W3137209623 · doi:10.1016/j.jhepr.2021.100265

Defatting strategies in the current era of liver steatosis

2021· review· en· W3137209623 on OpenAlex
Laura Mazilescu, Markus Selzner, Nazia Selzner

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJHEP Reports · 2021
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsHospital for Sick ChildrenToronto General Hospital
FundersDeutsche Forschungsgemeinschaft
KeywordsSteatosisDefattingMedicineFatty liverLiver transplantationLiver diseaseInternal medicineDiseaseTransplantationBiology

Abstract

fetched live from OpenAlex

Liver steatosis is emerging as a major cause of chronic liver disease worldwide, mainly due to the increasing rate of obesity, type 2 diabetes, and metabolic syndrome. Because of the increased incidence of liver steatosis, many organs are currently declined for transplantation despite high demand and waiting list mortality. Defatting strategies have recently emerged as a means of rapidly reducing liver steatosis to expand the pool of available organs. This review summarises advances in defatting strategies in experimental and human models of liver steatosis over the last 20 years.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.072
GPT teacher head0.379
Teacher spread0.306 · 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