Reduced Prevalence of Alcoholic Gastritis in Hospitalized Individuals Who Consume Cannabis
Bibliographic record
Abstract
BACKGROUND: Alcoholic gastritis, a superficial erosive disease of the stomach, is a common manifestation of risky alcohol use. In contrast, cannabis which is frequently co-used with alcohol suppresses gastric acidity and might counteract the deleterious effect of alcohol on the gastric mucosa. However, no clinical study has examined the impact of cannabis use on the development of alcoholic gastritis among risky alcohol users. METHODS: We analyzed hospital discharge records of adults (age ≥ 18 years), from 2014 of the Nationwide Inpatient Sample, with a diagnosis of risky alcohol use (n = 316,916). We used a propensity-based matching algorithm to match cannabis users to nonusers on 1:1 ratio (30,713: 30,713). We then measured the adjusted relative risk (aRR) for having alcoholic gastritis using conditional Poisson regression models with generalized estimating equations. RESULTS: Our study revealed that among risky alcohol users, cannabis co-users have a lower prevalence of alcoholic gastritis compared to noncannabis users (1,289 [1,169 to 1,421] vs. 1,723 [1,583 to 1,875] per 100,000 hospitalizations for risky alcohol use), resulting in a 25% decreased probability of alcoholic gastritis (aRR: 0.75 [0.66 to 0.85]; p-value <0.0001). Furthermore, dependent cannabis usage resulted in a lower prevalence of alcoholic gastritis when compared to both nondependent cannabis users (0.72 [0.52 to 0.99]) and to noncannabis users (0.56 [0.41 to 0.76]). CONCLUSIONS: We reveal that risky alcohol drinking combined with cannabis use is associated with reduced prevalence of alcohol-associated gastritis in patients. Given increased cannabis legislation globally, understanding whether and how the specific ingredients in cannabis plant extract can be used in the treatment of alcoholic gastritis is paramount. In this regard, further molecular mechanistic studies are needed to delineate the mechanisms of our novel findings not only for alcoholic gastritis but also for gastritis from other causes.
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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 | 0.004 |
| 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 | 0.000 |
| 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 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".