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Record W1495291850 · doi:10.1155/2007/231636

Complementary and Alternative Medicine Use by Patients Chronically Infected with Hepatitis C Virus

2007· article· en· W1495291850 on OpenAlexaffvenue
Colin White, Gerilynn Hirsch, Sunil Patel, Fatin Adams, Kevork Peltekian

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2007
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsRockyview General HospitalCapital District Health AuthorityDalhousie University
Fundersnot available
KeywordsVirologyHepatitis virusMedicineHepatitis C virusVirusTraditional medicine

Abstract

fetched live from OpenAlex

Complementary and alternative medicine (CAM) is becoming increasingly popular in North America. The use of CAM is also popular in patients with chronic liver disease but is not well documented. The extent of use of CAM in chronic hepatitis C virus (HCV) infected patients was determined, and the demographic and clinical data between users and nonusers of CAM was compared. Seventy-six patients (30% female) with chronic HCV were interviewed. The mean age was 43+/-8 years. Current use of CAM for HCV was reported by 35 of 76 patients (46%). Eighteen of 76 patients within this group used herbal supplements (24%). The most commonly used herb was Silybum marianum (milk thistle), reported by 10 of 76 patients (13.2%). Commonly reported benefits of CAM use included reduction in fatigue, boost in the immune system and improved gastrointestinal function. No adverse effects of CAM use were reported. In the present study, four of 18 patients (22%) with chronic liver disease taking herbal therapies were on herbs that increased bleeding time. The use of CAM in chronic HCV patients is significant. Patients should be asked specifically about their use of CAM. CAM use may have implications affecting conventional treatment and management of HCV.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.018
GPT teacher head0.266
Teacher spread0.248 · 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.

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

Citations43
Published2007
Admission routes2
Has abstractyes

Explore more

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