Significant reductions in alcohol use after hepatitis C treatment: results from the ANRS CO13‐HEPAVIH cohort
Bibliographic record
Abstract
BACKGROUND AND AIMS: Few data exist on changes to substance use patterns before and after hepatitis C virus (HCV) treatment. We used longitudinal data of HIV-HCV co-infected individuals to examine whether receiving pegylated interferon (Peg-IFN)-based therapy irrespective of HCV clearance could modify tobacco, cannabis and alcohol use. DESIGN: A prospective cohort of HIV-HCV co-infected individuals was enrolled from 2006. Participants' clinical data were retrieved from medical records and socio-demographic and behavioural characteristics were collected by yearly self-administered questionnaires. SETTING: Data were collected across 17 hospitals in France. PARTICIPANTS: All HIV-HCV co-infected patients who initiated HCV treatment during follow-up and answered items regarding substance use in at least one yearly questionnaire (258 patients, 671 visits). INTERVENTION: HCV treatment consisted of Peg-IFN-based regimens. MEASUREMENTS: Four time-varying outcomes: hazardous alcohol use (Alcohol Use Disorders Identification Test-C > 3/4 for women/men), number of alcohol units/month, binge drinking, cannabis and tobacco use. Mixed models assessed the effect of HCV treatment status (not yet treated, treated and HCV-cleared, treated and HCV-chronic) on each outcome. FINDINGS: A significant decrease (more than 60% reduction) in both hazardous alcohol use and binge drinking and a reduction of 10 alcohol units/month was observed after HCV treatment (irrespective of HCV clearance). No significant effect of HCV treatment status was found on tobacco use and regular cannabis use, but HCV 'clearers' reported less non-regular use of cannabis. CONCLUSIONS: Hepatitis C virus (HCV) treatment appears to help HIV-HCV co-infected patients reduce alcohol use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".