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Record W4283399224 · doi:10.3138/canlivj-2022-0005

Unconjugated hyperbilirubinemia may exacerbate certain underlying chronic liver diseases

2022· article· en· W4283399224 on OpenAlexaffvenue
Roman Dascal, Julia Uhanova, Gerald Y. Minuk

Bibliographic record

VenueCanadian Liver Journal · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeme Oxygenase-1 and Carbon Monoxide
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineInternal medicineGastroenterologyChronic liver diseaseLiver diseaseLiver function testsFibrosisAlcoholic liver diseaseHepatic fibrosisDiseaseFatty liverAlbuminLiver functionCirrhosis

Abstract

fetched live from OpenAlex

BACKGROUND: Negative correlations have been described between elevated serum unconjugated bilirubin levels and the prevalence/severity of various chronic inflammatory conditions. Whether a similar association exists for patients with unconjugated hyperbilirubinemia (UCB) and underlying chronic liver diseases (CLD) has yet to be reported. The aim of this study was to document hepatic necro-inflammatory disease activity and fibrosis in CLD patients with and without UCB and otherwise normal liver function tests (albumin and INR). METHODS: Necro-inflammatory disease activity was assessed by serum aminotransferase levels and fibrosis by APRI and FIB-4 calculations. UCB patients were matched 1:2 by age, gender and underlying CLD to patients with normal bilirubin levels. RESULTS: From a database of 9,745 CLD patients, 208 (2.1%) had UCB and 399 served as matched controls. Overall, UCB patients had significantly higher serum aminotransferase levels, APRI and FIB-4 scores. The differences were driven by patients with underlying chronic viral or immune mediated liver disorders rather than non-alcoholic fatty liver disease, alcohol related liver disease, or ‘other’ CLDs. CONCLUSIONS: These results suggest UCB is associated with increased rather than decreased hepatic necro-inflammatory disease activity and fibrosis in patients with certain CLDs.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.231
Teacher spread0.213 · 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.

Study designNot applicable
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
Published2022
Admission routes2
Has abstractyes

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