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Record W1969329034 · doi:10.1002/hep.22044

Herbal product use by persons enrolled in the hepatitis C Antiviral Long-Term Treatment Against Cirrhosis (HALT-C) Trial

2007· article· en· W1969329034 on OpenAlexfundno aff
Leonard B. Seeff, Teresa M. Curto, Gyöngyi Szabó, Gregory T. Everson, Herbert L. Bonkovsky, Jules L. Dienstag, Mitchell L. Shiffman, Karen L. Lindsay, Anna S. Lok, Adrian M. Di Bisceglie, William M. Lee, Marc G. Ghany

Bibliographic record

VenueHepatology · 2007
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health DisparitiesNational Center for Research ResourcesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthValeant Pharmaceuticals InternationalUniversity of Colorado School of Medicine, Anschutz Medical CampusUniversity of ConnecticutVertex PharmaceuticalsNew England Research InstitutesVirginia Commonwealth UniversityUniversity of Southern CaliforniaNational Cancer InstituteNational Institute on Alcohol Abuse and AlcoholismSaint Louis UniversityMassachusetts General HospitalGlaxoSmithKlineBristol-Myers SquibbEli Lilly and CompanyAstraZenecaUniversity of Washington
KeywordsMedicineCirrhosisHepatitis CTerm (time)Internal medicineGastroenterologyAntiviral treatmentVirologyChronic hepatitisVirus

Abstract

fetched live from OpenAlex

Herbal products, used for centuries in Far Eastern countries, are gaining popularity in western countries. Surveys indicate that persons with chronic hepatitis C (CHC) often use herbals, especially silymarin (milk thistle extract), hoping to improve the modest response to antiviral therapy and reduce side effects. The Hepatitis C Antiviral Long-Term Treatment Against Cirrhosis (HALT-C) Trial, involving persons with advanced CHC, nonresponders to prior antiviral therapy but still willing to participate in long-term pegylated interferon treatment, offered the opportunity to examine the use and potential effects of silymarin. Among 1145 study participants, 56% had never taken herbals, 21% admitted past use, and 23% were using them at enrollment. Silymarin constituted 72% of 60 herbals used at enrollment. Among all participants, 67% had never used silymarin, 16% used it in the past, and 17% used it at baseline. Silymarin use varied widely among the 10 participating study centers; men were more frequent users than women, as were non-Hispanic whites than African Americans and Hispanics. Silymarin use correlated strongly with higher education. No beneficial effect of silymarin was found on serum alanine aminotransferase or hepatitis C virus (HCV) RNA levels. Univariate analysis showed significantly fewer liver-related symptoms and better quality-of-life parameters in users than nonusers, but after reanalysis adjusted for covariates of age, race, education, alcohol consumption, exercise, body mass index, and smoking, only fatigue, nausea, liver pain, anorexia, muscle and joint pain, and general health remained significantly better in silymarin users. In conclusion, silymarin users had similar alanine aminotransferase and HCV levels to those of nonusers but fewer symptoms and somewhat better quality-of-life indices. Because its use among these HALT-C participants was self-motivated and uncontrolled, however, only a well-designed prospective study can determine whether silymarin provides benefit to persons with chronic hepatitis C.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.135
GPT teacher head0.416
Teacher spread0.281 · 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 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

Citations133
Published2007
Admission routes1
Has abstractyes

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