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Record W2980912411 · doi:10.3138/canlivj.2019-0005

Seasonal variability in the activity of common chronic liver diseases

2019· article· en· W2980912411 on OpenAlexaffvenue
Daniel Iluz‐Freundlich, Julia Uhanova, Gerald Y. Minuk

Bibliographic record

VenueCanadian Liver Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineInternal medicineLiver diseaseFatty liverChronic liver diseaseDyslipidemiaImmune systemAlcoholic liver diseaseVitamin D and neurologyObesityPhysiologyGastroenterologyDiseaseImmunologyCirrhosis

Abstract

fetched live from OpenAlex

BACKGROUND: Seasonal variations in flu-like illnesses and vaccinations, vitamin D levels, alcohol intake, and sedentary lifestyles raise the possibility that seasonal variations exist in the severity of immune-mediated, alcohol, and obesity- or dyslipidemia-related chronic liver diseases, respectively. METHODS: We documented months-seasons in which biochemical evidence of disease activity is greatest in adult patients with common liver disorders. Months-seasons associated with peak liver enzyme levels in patients with largely immune-mediated disorders (autoimmune hepatitis, primary biliary cholangitis [PBC], and primary sclerosing cholangitis), alcoholic liver disease, and non-alcoholic fatty liver disease were documented from a hospital-based, liver diseases outpatient clinic database. RESULTS: < .005), no significant associations were found between months-seasons and peak liver enzyme activities in any of these liver disorders. CONCLUSIONS: These findings suggest that seasonal illnesses or immunizations and vitamin D depletion, alcohol intake, and sedentary lifestyle do not significantly exacerbate common underlying immune-mediated, alcohol, or metabolic liver disorders, respectively.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.235
Teacher spread0.223 · 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 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

Citations3
Published2019
Admission routes2
Has abstractyes

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