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Record W4396905002 · doi:10.3138/canlivj-2024-0003

Population-level cascade of care for hepatitis C in Newfoundland and Labrador

2024· article· en· W4396905002 on OpenAlexaffvenueabout
Cindy Whitten, Alison Turner, Kobe Roberts, Brittany Howell, Brooklyn Sparkes, Peter Daley

Bibliographic record

VenueCanadian Liver Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsNewfoundland and Labrador Centre for Applied Health ResearchGovernment of Newfoundland and LabradorMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineHepatitis C virusMedical prescriptionPopulationAntibodyHepatitis CInternal medicineObservational studyPharmacyVirologyImmunologyPediatricsFamily medicineVirusEnvironmental healthPharmacology

Abstract

fetched live from OpenAlex

Background: Global elimination of hepatitis C virus (HCV) is feasible using existent tools. Reporting the provincial HCV care cascade will contribute to national and global HCV elimination efforts. Methods: This observational study was a secondary use of population-level medical record data, including laboratory results for HCV testing and prescription data for HCV treatment in the province of Newfoundland and Labrador (NL). All patients with HCV antibody testing performed between Jan 1, 2017 and Jan 1, 2022 were included. All prescriptions dispensed from a community pharmacy in NL for any HCV treatment during the same period were included. Results: There were 84,252 antibody tests included. Of these, 3,626 (4.3%) tests were positive for HCV antibodies. Seventy eight percent (1,377/1,766) of the individuals with positive antibody tests were tested for HCV RNA. Only 377/1,061 (35.5%) individuals with a positive RNA test were treated, and 257/395 (65.1%) achieved sustained virological response at 12 weeks. Conclusions: NL has successfully identified and treated HCV, but treatment access is low. Targets for improvement include increased screening, reflex testing of positive antibody with RNA, increased linkage to care, change in treatment funding policy, and quicker treatment funding decision.

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 score0.321
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.033
GPT teacher head0.313
Teacher spread0.279 · 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

Citations1
Published2024
Admission routes3
Has abstractyes

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