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S1148 Effect of Cannabis Use on Progression of Non-Alcoholic Fatty Liver Disease in Obese Patients: A Propensity-Matched Retrospective Cohort Study

2020· article· en· W3094596854 on OpenAlexaff
Ikechukwu Achebe, Chimezie Mbachi, Yuchen Wang, Ezekiel Chukwujindu, Isaac Paintsil, Bashar M. Attar

Bibliographic record

VenueThe American Journal of Gastroenterology · 2020
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineFatty liverSteatohepatitisInternal medicineHepatocellular carcinomaCannabisGastroenterologyCirrhosisLiver diseaseAlcoholic liver diseaseDiseasePsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Non-alcoholic fatty liver disease (NAFLD) describes a condition of pathologic fat accumulation in hepatocytes when a secondary cause cannot be identified (e.g. excessive alcohol use). The spectrum of disease seen in NAFLD includes Non-Alcoholic Fatty Liver (NAFL), and the more pathologic variant, Non-Alcoholic Steatohepatitis (NASH). While NAFL is more common, the hepatocellular injury and inflammation that occurs in NASH contributes significantly to the increased risk of liver failure, cirrhosis, and hepatocellular carcinoma in NAFLD patients. Anti-inflammatory effects of cannabis are well described in experimental literature. Tetrahydrocannabinol (THC) specifically, has been shown to have anti-inflammatory and hepatoprotective activity in myofibroblast and stellate cells. How the hepatoprotective properties of cannabis affect disease manifestation and incidence of NAFLD is the subject of this investigation. The aim of this study is to determine how cannabis use affects the prevalence and progression of NAFLD in obese human subjects. METHODS: We studied the 2016 Healthcare Cost and Utilization Project’s (HCUP) National Inpatient Sample (NIS) discharge records, and gathered data on patients who were obese and at least 18 years of age (N = 879952). The main study outcome was prevalence of the four presentations of NAFLD: Steatosis, Steatohepatitis, Cirrhosis and Hepatocellular Carcinoma. We then compared the prevalence of disease stage between cannabis and non-cannabis users. Lastly, we further matched the non-cannabis group with demographic factors and other patient related confounders. All analysis was done using STATA 14 software. RESULTS: A total of 879,952 obese patients were admitted within the study period. Cannabis users 14,236 (1.6%) had less steatohepatitis (0.4% vs 0.7%, P < 0.001) and cirrhosis (1.1% vs 1.5%, P < 0.001) than non-users. After propensity matched analysis, cannabis use remained significantly associated with less steatohepatitis (0.4% vs 0.5%, P = 0.035). Post-match, there was no statistically significant difference in the prevalence of NAFL, cirrhosis and hepatocellular carcinoma. CONCLUSION: Results from this study suggest that cannabis use is associated with reduced prevalence and progression of steatohepatitis in obese patients. These findings could possibly be explained by the anti-inflammatory and hepatoprotective effect of cannabis on hepatocytes through the endocannabinoid system. Additional studies are needed to further explore this relationship.Table 1a.: Descriptive Statistics of Obese Population: Cannabis and Non-Cannabis Users and Propensity Score MatchTable 1b.: Outcome Statistics of Obese Population: Cannabis and Non-Cannabis Users and Propensity Score Match

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.012
GPT teacher head0.276
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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