Marijuana Smoking Does Not Accelerate Progression of Liver Disease in HIV–Hepatitis C Coinfection: A Longitudinal Cohort Analysis
Bibliographic record
Abstract
Background. Marijuana smoking is common and believed to relieve many symptoms, but daily use has been as-sociated with liver fibrosis in cross-sectional studies. We aimed to estimate the effect of marijuana smoking on liver disease progression in a Canadian prospective multicenter cohort of human immunodeficiency virus/hepatitis C virus (HIV/HCV) coinfected persons. Methods. Data were analyzed for 690 HCV polymerase chain reaction positive (PCR-positive) individuals without significant fibrosis or end-stage liver disease (ESLD) at baseline. Time-updated Cox Proportional Hazards models were used to assess the association between the average number of joints smoked/week and progression to significant liver fibrosis (APRI ≥ 1.5), cirrhosis (APRI ≥ 2) or ESLD. Results. At baseline, 53 % had smoked marijuana in the past 6 months, consuming a median of 7 joints/week (IQR, 1–21); 40 % smoked daily. There was no evidence that marijuana smoking accelerates progression to signifi-cant liver fibrosis (APRI ≥ 1.5) or cirrhosis (APRI ≥ 2; hazard ratio [HR]: 1.02 [0.93–1.12] and 0.99 [0.88–1.12], re-spectively). Each 10 additional joints/week smoked slightly increased the risk of progression to a clinical diagnosis of cirrhosis and ESLD combined (HR, 1.13 [1.01–1.28]). However, when exposure was lagged to 6–12 months before the diagnosis, marijuana was no longer associated with clinical disease progression (HR, 1.10 [0.95–1.26]). Conclusions. In this prospective analysis we found no evidence for an association between marijuana smoking
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".