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Record W2414923052 · doi:10.1097/coh.0000000000000183

Epidemiology of hepatitis C virus in HIV-infected patients

2015· review· en· W2414923052 on OpenAlexaff
Lars Peters, Marina B. Klein

Bibliographic record

VenueCurrent Opinion in HIV and AIDS · 2015
Typereview
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsVirologyEpidemiologyMedicineHuman immunodeficiency virus (HIV)Hepatitis virusHepatitis C virusVirusInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review will give an update on the prevalence of HIV/hepatitis C virus (HCV) coinfection, and describe recent trends in all-cause and cause-specific mortality. The focus is mainly on patients followed in clinics in high-income countries and their heterogeneity in terms of risk factors and clinical outcomes. RECENT FINDINGS: In countries that have introduced comprehensive preventive strategies for injection drug users, the prevalence of HIV/HCV coinfection has declined. Compared with HIV monoinfected patients, the mortality among HCV-coinfected patients remains markedly increased because of multiple risk factors, in particular among drug users. The spectrum of causes of death among coinfected has recently been described in large cohort studies. Around a quarter of all deaths were liver related, and the incidence has decreased in Western Europe and stabilized in Eastern Europe where AIDS remains the dominant cause of death. In North America, the incidence of end-stage liver disease has not decreased despite improvements in HIV care. HCV treatment seems to have had little effect thus far on mortality at the population level. SUMMARY: Despite a decreasing prevalence of HIV/HCV coinfection in many countries, coinfection remains an important clinical problem that requires a multidisciplinary approach including direct-acting antivirals for those at risk of liver-related death.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.239
GPT teacher head0.479
Teacher spread0.240 · 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.

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

Citations41
Published2015
Admission routes1
Has abstractyes

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