Effectiveness of chronic hepatitis C treatment in drug users in routine clinical practice: results of a prospective cohort study
Bibliographic record
Abstract
OBJECTIVE: Injection drug users are often excluded from hepatitis C virus (HCV) treatment. This study compares sustained virological response, adherence, and quality of life in patients with or without a history of illicit drug use in routine clinical practice. METHODS: This is a post-hoc analysis of a prospective, observational study conducted in 1860 patients who received peginterferon alpha-2b/ribavirin combination therapy. Nondrug users (NDUs) were defined as patients without a history of drug addiction; former drug users (FDUs) as patients who had stopped using illicit drugs or opioid maintenance therapy and active drug users (ADUs) as patients using illicit drugs or on opioid maintenance therapy. Virological response, adherence, and the health-related quality of life were assessed by the measure of HCV RNA in the serum, self-report and 36-item short-form health survey Questionnaire, respectively. RESULTS: The analyzed population included 1038 (56%) NDUs, 578 (31%) FDUs, and 244 (13%) ADUs. About 85% of ADUs were on opioid maintenance therapy and 25% used illicit drugs. Although ADUs had a more chaotic lifestyle and more psychiatric disorders, sustained virological response of ADUs (58%) did not differ from that of NDUs (49%) and FDUs (51%) (P=0.133). Adherence rates were 39% in NDUs and FDUs, and 37% in ADUs (P=0.883). Health-related quality of life was improved in the three groups after the end of treatment. CONCLUSION: Our study suggests that HCV therapy in ADUs on opioid maintenance therapy is as effective as in other HCV patients. The effectiveness of HCV therapy in illicit drug users needs to be evaluated in further studies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".