Optimizing assessment and treatment for hepatitis C virus infection in illicit drug users: a novel model incorporating multidisciplinary care and peer support
Bibliographic record
Abstract
OBJECTIVES: We evaluated assessment and treatment for hepatitis C virus (HCV) among illicit drug users accepting referral to a weekly HCV peer-support group at a multidisciplinary community health centre. METHODS: From March 2005 to 2008, HCV-infected individuals were referred to a weekly peer-support group and assessed for HCV infection. A retrospective chart review of outcomes 3 years after the initiation of the group was conducted (including HCV assessment and treatment). RESULTS: Two hundred and four HCV antibody-positive illicit drug users accepted referral to a weekly HCV peer-support group. Assessment for HCV occurred in 53% of patients(n= 109), with 13% (n= 14) having initiated or completed treatment for HCV infection before attending the support group, evaluation ongoing in 10% (n= 11) and treatment deferred/not indicated in 25% (n= 27). The major reasons for HCV treatment deferral included early disease (30%),drug dependence (37%), other medical (11%) or psychiatric comorbidities (4%). Sixty-eight percent of those deferred for reasons other than early liver disease showed multiple reasons for treatment deferral. The first 4 weeks of support group attendance predicted successful HCV assessment (odds ratio: 6.03, 95% confidence interval:3.27-11.12, P < 0.001). Overall, 28% (n= 57) received treatment. Among individuals having completed pegylated-interferon and ribavirin therapy with appropriate follow-up (n =19), the rate of sustained virologic response was 63% (12/19), despite illicit drug use in 53%. CONCLUSION: A high proportion of illicit drug users accepting referral to a weekly HCV peer-support group at a multidisciplinary health centre were assessed and treated for HCV infection. Peer support coupled with multidisciplinary care is an effective strategy for engaging illicit drug users in HCV care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".