Cannabis use is associated with reduced prevalence of progressive stages of alcoholic liver disease
Bibliographic record
Abstract
BACKGROUND: Abusive alcohol use has well-established health risks including causing liver disease (ALD) characterized by alcoholic steatosis (AS), steatohepatitis (AH), fibrosis, cirrhosis (AC) and hepatocellular carcinoma (HCC). Strikingly, a significant number of individuals who abuse alcohol also use Cannabis, which has seen increased legalization globally. While cannabis has demonstrated anti-inflammatory properties, its combined use with alcohol and the development of liver disease remain unclear. AIM: The aim of this study was to determine the effects of cannabis use on the incidence of liver disease in individuals who abuse alcohol. METHODS: We analysed the 2014 Healthcare Cost and Utilization Project-Nationwide Inpatient Sample (NIS) discharge records of patients 18 years and older, who had a past or current history of abusive alcohol use (n = 319 514). Using the International Classification of Disease, Ninth Edition codes, we studied the four distinct phases of progressive ALD with respect to three cannabis exposure groups: non-cannabis users (90.39%), non-dependent cannabis users (8.26%) and dependent cannabis users (1.36%). We accounted for the complex survey sampling methodology and estimated the adjusted odds ratio (AOR) for developing AS, AH, AC and HCC with respect to cannabis use (SAS 9.4). RESULTS: Our study revealed that among alcohol users, individuals who additionally use cannabis (dependent and non-dependent cannabis use) showed significantly lower odds of developing AS, AH, AC and HCC (AOR: 0.55 [0.48-0.64], 0.57 [0.53-0.61], 0.45 [0.43-0.48] and 0.62 [0.51-0.76]). Furthermore, dependent users had significantly lower odds than non-dependent users for developing liver disease. CONCLUSIONS: Our findings suggest that cannabis use is associated with a reduced incidence of liver disease in alcoholics.
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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 | 0.003 |
| 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 | 0.001 |
| 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".