MétaCan
Menu
Back to cohort
Record W3110056565 · doi:10.14218/jcth.2020.00012

Chronic Liver Disease and Silymarin: A Biochemical and Clinical Review

2020· review· en· W3110056565 on OpenAlexaff
Sean Tighe, Daud Akhtar, Umair Iqbal, Aijaz Ahmed

Bibliographic record

VenueJournal of Clinical and Translational Hepatology · 2020
Typereview
Languageen
FieldMedicine
TopicSilymarin and Mushroom Poisoning
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMilk ThistleSilybum marianumMedicineBioavailabilitySilibininPharmacologyFatty liverDrugLiver diseasePharmacokineticsChronic liver diseaseDiseaseAlcoholic liver diseaseDosingTraditional medicineInternal medicineCirrhosis

Abstract

fetched live from OpenAlex

Chronic liver disease (CLD) is an under-recognized epidemic that continues to increase in prevalence and is a major health concern. Silymarin, the active compound of Silybum marianum (Milk thistle), has historically been used in CLD. A significant barrier to silymarin use is its poor bioavailability. Attempts at improving the bioavailability of silymarin have led to a better understanding of formulation methods, pharmacokinetics, dosing, and associated drug interactions. Clinically, silymarin exerts its hepatoprotective effects through antioxidative, antifibrotic, anti-inflammatory, antitoxin, and anticancerous mechanisms of actions. Despite the use of silymarin being extensively studied in alcoholic liver disease, metabolic-associated fatty liver disease, viral hepatitis, and drug-induced liver injury, the overall efficacy of silymarin remains unclear and more research is warranted to better elucidate the role of silymarin in CLD, specifically regarding its anti-inflammatory effects. Here, we review the current biochemical and clinical evidence regarding silymarin in CLD.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.146
GPT teacher head0.468
Teacher spread0.322 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations62
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical and Translational HepatologySame topicSilymarin and Mushroom PoisoningFrench-language works237,207