Opioid use and risk of liver fibrosis in<scp>HIV</scp>/hepatitis<scp>C</scp>virus‐coinfected patients in<scp>C</scp>anada
Bibliographic record
Abstract
OBJECTIVES: Opioid use and opioid-related mortality have increased dramatically since the 1990s in North America. The effect of opioids on the liver is incompletely understood. Some studies have suggested that opioids cause liver damage and others have failed to show any harm. HIV/hepatitis C virus (HCV)-coinfected persons may be particularly vulnerable to factors increasing liver fibrosis. We aimed to describe opioid use in an HIV/HCV-coinfected population in Canada and to estimate the association between opioid use and liver fibrosis. METHODS: We conducted a cross-sectional descriptive analysis of the Canadian Co-infection Cohort Study data to characterize opioid use. We then conducted a longitudinal analysis to assess the average change in aspartate aminotransferase-to-platelet ratio index (APRI) score associated with opioid use using a generalized estimating equation with linear regression. We assessed the progression to significant liver fibrosis (APRI ≥ 1.5) associated with opioid use with pooled logistic regression. RESULTS: In the 6 months preceding cohort entry, 32% of the participants had received an opioid prescription, 28% had used opioids illicitly and 18% had both received a prescription and used opioids illicitly. Neither prescribed nor illicit opioid use was associated with a change in the median APRI score [exp(β) 0.99 (95% confidence interval (CI) 0.82, 1.12) and exp(β) 0.95 (95% CI 0.81, 1.10), respectively] or with faster progression to liver fibrosis [hazard odds ratio (HOR) 1.20 (95% CI 0.73, 1.67) and HOR 1.09 (95% CI 0.63, 1.55), respectively]. CONCLUSIONS: Although opioids were commonly used both legally and illegally in our cohort, we were unable to demonstrate a negative impact on liver fibrosis progression.
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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 | 0.002 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".