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Record W3101458064 · doi:10.1016/j.aohep.2020.10.011

Identifying gaps in the treatment of hepatitis C in patients co-infected with HIV in Edmonton, Alberta

2020· article· en· W3101458064 on OpenAlexaffabout
Jessica M. Round, Bohdan Savaryn, Sabrina S. Plitt, Stephen D. Shafran, Carmen Charlton

Bibliographic record

VenueAnnals of Hepatology · 2020
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsPublic Health Agency of CanadaUniversity of AlbertaProvincial Laboratory of Public Health
Fundersnot available
KeywordsMedicineHepatitis C virusHepatitis CPopulationCohortInternal medicineImmunologyHuman immunodeficiency virus (HIV)VirologyVirus

Abstract

fetched live from OpenAlex

INTRODUCTION: With the availability of direct-acting antivirals, Hepatitis C (HCV) is now considered a treatable disease. Patients who are co-infected with human immunodeficiency virus (HIV) and HCV represent an ideal patient population to treat for HCV, as (1) patients are routinely taking medication for HIV, and therefore would be able to complete HCV drug regimens, and (2) HIV infection has been shown to increase HCV disease progression. OBJECTIVE: We sought to determine the occurrence of HCV co-infection among HIV patients in our provincial cohort, determine whether they received treatment for HCV, and identify currently viremic patients who can be linked to care. MATERIALS AND METHODS: HCV laboratory testing data (HCV antibody and HCV RNA) and HCV medication dispensation data was collected for all HIV positive patients. Current and previous HCV infection and treatment was assessed. Chart reviews were conducted for HCV viremic patients to assess their HIV care and social determinants. RESULTS: Of the 2417 HIV positive patients, 392 (16.2%) were identified as being co-infected with HCV. 198 (50.5%) of the HIV-HCV co-infected patients received HCV treatment and 232 (59.2%) were not viremic on the most recent HCV RNA test. 99 (69.2%) had a suppressed HIV infection suggesting they are active in their HIV care and good candidates for HCV treatment. CONCLUSION: Despite the availability of direct-acting antivirals, many patients who are co-infected with HIV and HCV are not being treated for HCV. Routine surveillance of HIV-HCV co-infected patients could improve HCV treatment rates in a high-risk population.

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 score0.033
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.079
GPT teacher head0.373
Teacher spread0.294 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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