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Record W2157680862 · doi:10.1093/cid/cit301

Hepatitis C Virus Reinfection Following Treatment Among People Who Use Drugs

2013· review· en· W2157680862 on OpenAlexaff
Bart Grady, Janke Schinkel, Xiomara V. Thomas, Olav Dalgård

Bibliographic record

VenueClinical Infectious Diseases · 2013
Typereview
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsMedicineHepatitis C virusIncidence (geometry)ImmunologyPopulationHepatitis CVirusViral diseaseImmunityDrugVirologyInternal medicineImmune systemEnvironmental healthPharmacology

Abstract

fetched live from OpenAlex

Most new cases of hepatitis C virus (HCV) infections in the developed world are associated with injection drug use. However, treatment for people who inject drugs (PWID) is controversial, as successful treatment risks being followed by new infection. Reinfection after sustained virologic response has been reported, but is the risk so great that treatment should be withheld from this large HCV population? Preliminary evidence suggests that the reinfection incidence is low, but studies to date have been limited by small sample size and few cases of reinfection. In this review, we assess data from studies among PWID of HCV reinfection following treatment to give a reasonable estimate on how frequently reinfection appears and try to characterize those most at risk, The observation that spontaneous clearance of HCV reinfection following treatment occurs is suggestive of a partial protective immunity against persistent infection.

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.113
GPT teacher head0.447
Teacher spread0.334 · 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
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

Citations96
Published2013
Admission routes1
Has abstractyes

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