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Record W2794007574 · doi:10.14745/ccdr.v42i03a02

Hepatitis C in Canada and the importance of risk-based screening

2016· article· en· W2794007574 on OpenAlexaffvenueabout
S Ha, S Totten, L Pogany, Jun Wu, M Gale-Rowe

Bibliographic record

VenueCanada Communicable Disease Report · 2016
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsMedicineHepatocellular carcinomaHepatitis CHepatitis C virusHealth careCirrhosisPublic healthPopulationHepatitisEnvironmental healthFamily medicineImmunologyInternal medicineVirusPathology

Abstract

fetched live from OpenAlex

Chronic hepatitis C (CHC) remains a public health issue affecting an estimated 220,000 individuals in Canada. In 2011, approximately 44% of those with CHC were unaware of their infection. Hepatitis C is infectious in origin, and if left untreated, can lead to significant morbidity and mortality in its chronic form, including liver cirrhosis, hepatocellular carcinoma and liver failure. These health outcomes are associated with comorbidities, adding a burden to the Canadian health care system. Recent advancements in the treatment of hepatitis C have changed the clinical landscape. In Canada, the prevalence of incident cases is higher in specific population groups. Injection drug use (IDU) currently accounts for the highest proportion of new hepatitis C virus (HCV) infection. It is unclear to what extent HCV infection through health care or personal services use contributed to current prevalent cases of CHC. The Canadian Task Force on Preventive Health Care (CTFPHC) is currently reviewing the evidence for different approaches to HCV screening and the benefits and harms of screening. Risk-based screening remains critical to detecting hepatitis C as knowing one's status has been linked to the cascade of care and improved population health outcomes. This article intends to highlight risk factors associated with the acquisition of HCV so that health care providers can screen, where appropriate, and detect CHC.

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)0.003
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 score0.021
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.262
Teacher spread0.244 · 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

Citations32
Published2016
Admission routes3
Has abstractyes

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