High proportions of liver fibrosis and cirrhosis in an ageing population of people who use drugs in Amsterdam, The Netherlands
Bibliographic record
Abstract
BACKGROUND: The incidence and prevalence of hepatitis C virus (HCV) infection among people who use drugs (PWUD) peaked in the 1980s in Amsterdam. As liver cirrhosis develops several decades after HCV infection and PWUD have other risk factors for liver fibrosis, we hypothesized that significant liver fibrosis or cirrhosis is now common among PWUD in Amsterdam. METHODS: PWUD were recruited from the Amsterdam Cohort Studies, methadone programmes and addiction clinics during 2009-2016. Transient elastography was performed to assess liver stiffness. We estimated METAVIR fibrosis levels on the basis of the following liver stiffness measurements (LSMs) cut-offs: F0-F2 (no/mild) less than 7.65 kPa; F2-F3 (moderate/severe) at least 7.65 to less than 13 kPa; and F4 (cirrhosis) at least 13 kPa. Using linear regression models, we assessed the association between LSM and sociodemographic, clinical and behavioural determinants in (a) all PWUD and (b) chronic hepatitis C virus (cHCV)-infected PWUD. RESULTS: For 140 PWUD, the median LSM was 7.6 kPa (interquartile range=4.9-12.0); 26.4% had moderate/severe fibrosis and 22.9% had cirrhosis. Of 104 chronically infected PWUD, 57.7% had evidence of significant fibrosis (≥F2). In multivariable analysis including all PWUD, increased LSM was associated significantly with cHCV monoinfection and HIV/HCV coinfection. In cHCV-infected PWUD, older age was associated significantly with increased LSM. In all groups, longer duration of heavy alcohol drinking was associated with increased LSM. CONCLUSION: A high proportion of PWUD had significant fibrosis or cirrhosis that were associated with cHCV infection, HIV/HCV coinfection and duration of heavy alcohol drinking. Increased uptake of HCV treatment and interventions to reduce alcohol use are needed to decrease the liver disease burden in this population.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".