Reduced Incidence and Better Liver Disease Outcomes among Chronic HCV Infected Patients Who Consume Cannabis
Bibliographic record
Abstract
Background and Aim: The effect of cannabis use on chronic liver disease (CLD) from Hepatitis C Virus (HCV) infection, the most common cause of CLD, has been controversial. Here, we investigated the impact of cannabis use on the prevalence of CLD among HCV infected individuals. Methods: We analyzed hospital discharge records of adults (age ≥ 18 years) with a positive HCV diagnosis. We evaluated records from 2007 to 2014 of the Nationwide Inpatient Sample (NIS). We excluded records with other causes of chronic liver diseases (alcohol, hemochromatosis, NAFLD, PBC, HBV, etc.). Of the 188,333 records, we matched cannabis users to nonusers on 1:1 ratio (4,728:4,728), using a propensity-based matching system, with a stringent algorithm. We then used conditional regression models with generalized estimating equations to measure the adjusted prevalence rate ratio (aPRR) for having liver cirrhosis (and its complications), carcinoma, mortality, discharge disposition, and the adjusted mean ratio (aMR) of total hospital cost and length of stay (LOS) [SAS 9.4]. Results: Our study revealed that cannabis users (CUs) had decreased prevalence of liver cirrhosis (aPRR: 0.81[0.72-0.91]), unfavorable discharge disposition (0.87[0.78-0.96]), and lower total health care cost ($39,642[36,220-43,387] versus $45,566[$42,244-$49,150]), compared to noncannabis users (NCUs). However, there was no difference among CUs and NCUs on the incidence of liver carcinoma (0.79[0.55-1.13]), in-hospital mortality (0.84[0.60-1.17]), and LOS (5.58[5.10-6.09] versus 5.66[5.25-6.01]). Among CUs, dependent cannabis use was associated with lower prevalence of liver cirrhosis, compared to nondependent use (0.62[0.41-0.93]). Conclusions: Our findings suggest that cannabis use is associated with decreased incidence of liver cirrhosis, but no change in mortality nor LOS among HCV patients. These novel observations warrant further molecular mechanistic studies.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".