Cannabis Use Provides Symptom Relief in Patients with Inflammatory Bowel Disease but Is Associated with Worse Disease Prognosis in Patients with Crohnʼs Disease
Bibliographic record
Abstract
BACKGROUND: Cannabinoids are used by patients with inflammatory bowel disease (IBD) to alleviate their symptoms. Little is known on patient motivation, benefit, or risks of this practice. Our aim was to assess the extent and motives for Cannabis use in patients with IBD and the beneficial and adverse effects associated with self-administration of Cannabis. METHODS: Consecutive patients with IBD (n = 313) seen in the University of Calgary from July 2008 to March 2009 completed a structured anonymous questionnaire covering motives, pattern of use, and subjective beneficial and adverse effects associated with self-administration of Cannabis. Subjects who had used Cannabis specifically for the treatment of IBD or its symptoms were compared with those who had not. Logistic regression analysis was used to identify variables predictive of poor IBD outcomes, specifically surgery or hospitalization for IBD. RESULTS: Cannabis had been used by 17.6% of respondents specifically to relieve symptoms associated with their IBD, the majority by inhalational route (96.4%). Patients with IBD reported that Cannabis improved abdominal pain (83.9%), abdominal cramping (76.8%), joint pain (48.2%), and diarrhea (28.6%), although side effects were frequent. The use of Cannabis for more than 6 months at any time for IBD symptoms was a strong predictor of requiring surgery in patients with Crohn's disease (odds ratio = 5.03, 95% confidence interval = 1.45-17.46) after correcting for demographic factors, tobacco smoking status, time since IBD diagnosis, and biological use. Cannabis was not a predictor for hospitalization for IBD in the previous year. CONCLUSIONS: Cannabis use is common in patients with IBD and subjectively improved pain and diarrheal symptoms. However, Cannabis use was associated with higher risk of surgery in patients with Crohn's disease. Patients using Cannabis should be cautioned about potential harm, until clinical trials evaluate efficacy and safety.
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 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".