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Record W2129683974 · doi:10.1155/2007/138245

Management of Chronic Hepatitis C: Consensus Guidelines

2007· article· en· W2129683974 on OpenAlexaffvenueabout
Morris Sherman, Stephen D. Shafran, Kelly W. Burak, Karen Doucette, Winnie Wong, Nigel Girgrah, Eric M. Yoshida, Eberhard L. Renner, Philip Wong, Marc Deschênes

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2007
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsUniversity of ManitobaUniversity of British ColumbiaMcGill UniversityUniversity of CalgaryUniversity of AlbertaUniversity of Toronto
Fundersnot available
KeywordsConsensus conferenceChronic hepatitisMedicineHepatitisEpidemiologyIntensive care medicineHepatitis CViral hepatitisFamily medicineImmunologyPathologyInternal medicineVirus

Abstract

fetched live from OpenAlex

Since the last consensus conference on the management of chronic viral hepatitis, a number of studies looking at modifications of the standard course of treatment have been published. These changes have been sufficiently substantive to warrant review to determine whether any changes in the recommended treatment algorithms are needed. A consensus development conference was held in January 2007, and the present document highlights the results of the presentations and discussion about these issues. It reviews the epidemiology of hepatitis C in Canada, treatment of acute hepatitis C and new algorithms in chronic hepatitis C, including retreatment of previous treatment failures. In addition, sections on management of hepatitis C in special populations have been updated. There is also a section on the use of hematopoietic growth factors to help manage patients on therapy. The document should be read in conjunction with the previous document to identify changes. Some recommendations made in the previous document remain and are not discussed here.

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)0.023
metaresearch head score (Gemma)0.041
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.041
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0060.006
Science and technology studies0.0020.002
Scholarly communication0.0040.004
Open science0.0080.003
Research integrity0.0090.010
Insufficient payload (model declined to judge)0.0070.007

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 head0.040
GPT teacher head0.333
Teacher spread0.293 · 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
GenreOther

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

Citations141
Published2007
Admission routes3
Has abstractyes

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Same venueCanadian Journal of GastroenterologySame topicHepatitis C virus researchFrench-language works237,207