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Record W2888706616 · doi:10.2196/10313

Hepatitis C Prevalence and Management Among Patients Receiving Opioid Substitution Treatment in General Practice in Ireland: Baseline Data from a Feasibility Study

2018· article· en· W2888706616 on OpenAlexvenueno aff
Ross Murtagh, Davina Swan, Eileen O’Connor, Geoff McCombe, John S. Lambert, Gordana Avramovic, Walter Cullen

Bibliographic record

VenueInteractive Journal of Medical Research · 2018
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHepatitis CHepatitis C virusTransmission (telecommunications)Injection drug useInternal medicineOpioidAntiviral therapyDrugLiver diseaseVirologyChronic hepatitisVirusPharmacologyDrug injection

Abstract

fetched live from OpenAlex

BACKGROUND: Hepatitis C virus (HCV) infection is a major cause of chronic liver disease and death. Injection drug use is now one of the main routes of transmission of HCV in Ireland and globally with an estimated 80% new infections occurring among people who inject drugs (PWID). OBJECTIVE: We aimed to examine whether patients receiving opioid substitution therapy in primary care practices in Ireland were receiving guideline-adherent care regarding HCV screening. Ireland has developed a model of care for delivering opioid substitution treatment in the primary care setting. We conducted this study given the shift of providing care for PWID from secondary to primary care settings, in light of current guidelines aimed at scaling up interventions to reduce chronic HCV infection and associated mortality. METHODS: We included baseline data from the Dublin site of the Heplink study, a feasibility study focusing on developing complex interventions to enhance community-based HCV treatment and improve the HCV care pathway between primary and secondary care. We recruited 14 opioid substitution treatment-prescribing general practices that employed the administration of opioid substitution therapy from the professional networks and databases of members of the research consortium. A standardized nonprobability sampling framework was used to identify 10 patients from each practice to participate in the study. Patients were eligible if aged ≥18 years, on opioid substitution treatment, and attending the practice for any reason during the recruitment period. The baseline data were collected from the clinical records of participating patients. We collected and analyzed data on demographic characteristics, care processes and outcomes regarding HCV and other blood-borne viruses, urinalysis test results, alcohol use disorders, chronic illness, and health service utilization. We examined whether patients received care concordant with guidelines related to HCV screening and care. RESULTS: The baseline data were collected from clinical records of 134 patients; 72.2% (96/134) were males; (mean age 43, SD 7.6; range 27-71 years); 94.8% (127/134) of patients had been tested for anti-HCV antibody in their lifetime; of those, 77.9% (99/127) tested positive. Then, 83.6% (112/134) of patients had received an HIV antibody test in their lifetime; of those, 6.3% (7/112) tested HIV positive. Moreover, 66.4% (89/134) of patients had been tested for hepatitis B virus in their lifetime and 8% (7/89) of those were positive. In the 12 months before the study, 30.6% (41/134) of patients were asked about their alcohol use by their general practitioner, 6.0% (8/134) received a brief intervention, and 2.2% (3/134) were referred to a specialist addiction or alcohol treatment service. CONCLUSIONS: With general practice and primary care playing an increased role in HCV care, this study highlights the importance of prioritizing the development and evaluation of real-world clinical solutions that support patients from diagnosis to treatment completion.

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)0.013
metaresearch head score (Gemma)0.036
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.126
GPT teacher head0.495
Teacher spread0.369 · 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 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

Citations11
Published2018
Admission routes1
Has abstractyes

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