Relationship Between Recreational Cannabis Use and <i>Helicobacter pylori</i> Infection
Bibliographic record
Abstract
Background: Cannabis plant extracts suppress gastric acid secretion and inflammation, and promote gastroduodenal ulcer healing, all of which are triggered by Helicobacter Pylori infection (HPI). Here, we evaluate the association between cannabis use and HPI among a representative community sample. Materials and Methods: We identified respondents who completed cannabis use questions and were tested for HPI ( H. pylori IgG antibody seropositivity) from the National Health and Nutrition Examination Survey III dataset ( n =4556). Cannabis usage was categorized as ever-use (ever, never), cumulative lifetime use (>10-times, 1–10-times, never), or recent use (>31-days-ago, within-31-days, never). We calculated the crude and adjusted risk (prevalence rate ratio, cPRR and aPRR) of having HPI with cannabis use using generalized Poisson models (SAS 9.4). The models were adjusted for demographics and risk factors for HPI. Results: The prevalence of HPI was lower among ever versus never cannabis users (18.6% vs. 33%, p <0.0001). Cannabis use was associated with a decreased risk of HPI (cPRR: 0.56 confidence interval [95% CI: 0.47–0.67]; p <0.0001), which persisted after adjusting for demographics (aPRR: 0.75 [95% CI: 0.63–0.90]; p =0.0016) and comorbidities (aPRR: 0.79 [95% CI: 0.66–0.95]; p =0.0145). Further, individuals with >10-times lifetime cannabis use had a decreased risk of HPI compared with those with 1–10-times lifetime use (aPRR: 0.70 [95% CI: 0.55–0.89]; p =0.0011) and never-users (aPRR: 0.65 [95% CI: 0.50–0.84]; p =0.0002). Conclusion: Recreational cannabis use is associated with diminished risk of HPI. These observations suggest the need for additional research assessing the effects of medical cannabis formulations on HPI.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".