Cannabis use is associated with reduced prevalence of progressive stages of alcoholic liver disease
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it