Cannabis Use in Patients With Inflammatory Bowel Disease Following Legalization of Cannabis in Canada
Bibliographic record
Abstract
Background: Cannabis is used by patients with Crohn's disease (CD) and ulcerative colitis (UC) as an alternative to, or in combination with, conventional therapies to treat symptoms such as abdominal pain, poor sleep, and reduced appetite. The clinical efficacy of cannabis for these disorders is controversial, with some studies showing harmful outcomes associated with its use. Previous studies suggest that cannabis is used by ~12% of patients with UC and ~16% of patients with CD in the USA despite legal prohibition. Methods: We conducted a prospective cohort study of adult patients with inflammatory bowel diseases (IBD) followed in a Canadian tertiary care center. Patients completed an online 40-question survey that included demographics, IBD disease history, cannabis use, and the Short Inflammatory Bowel Disease Questionnaire (SIBDQ). Results: Completed surveys were obtained from 254 participants (148 with CD, 90 with UC, and 16 with indeterminate colitis). Recent cannabis use was reported by 41% of CD and 31% of UC participants. Interestingly, only 46% of participants who used cannabis discussed their use with their physician. Participants who recently used cannabis reported more abdominal pain, poor appetite, and flatulence, and importantly this was associated with lower SIBDQ scores (recent use 37 vs non-recent use 40). Conclusions: Cannabis use among patients with IBD has more than doubled since its legalization. Cannabis use is associated with worse abdominal symptoms and quality of life. Physicians should inquire about cannabis use and optimize symptom control with evidence-based therapies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".