Multiple viral hepatitis in injection drug users and associated risk factors
Bibliographic record
Abstract
BACKGROUND: While infections due to hepatitis B virus (HBV) and hepatitis C virus (HCV) have been well-studied in injection drug users (IDUs), hepatitis A virus (HAV) infection and coinfection with multiple hepatitis viruses have received less attention. METHODS: Hepatitis serology as well as sociodemographic and drug-related parameters were explored in patients (n = 1512) admitted for opiate detoxification. RESULTS: Antibodies to HAV were positive in 57.7%, to HBV in 53.0%, and to HCV in 75.0% of the sample. Lack of any hepatic marker was reported in 11.2%; one marker was positive in 24.7%; two markers were positive in 31.2%; and all markers were positive in 32.9%. In patients with one positive marker, 58.8% had had exposure to HCV, and 27% had exposure to HAV. In patients with two positive markers, 46.7% were HAV/HCV and 41.8% HBV/HCV antibody positive. Presence of HBV and HCV antibodies was associated with older age, longer duration of (i.v.) heroin use, and a higher number of rehabilitation treatment episodes (anova), current coconsumption of cocaine was associated with presence of antibodies to either HAV, HBV, and HCV. CONCLUSIONS: Coinfection with hepatic viruses is highly relevant in IDUs, although HAV does not necessarily share the same risk factors relevant for HBV or HCV transmission. The need for outreach vaccination programs is emphasized for HAV and HBV in the target population. Primary prevention should be implemented before initiation or at early stages of a drug career. Epidemiology and transmission of HAV in IDUs requires further research.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".