The Prevalence and Predictors of Opioid Use in Inflammatory Bowel Disease: A Population-Based Analysis
Bibliographic record
Abstract
OBJECTIVES: Opioids are commonly used in the treatment of pain and associated symptoms of inflammatory bowel disease (IBD). The continuous use of opioids has been associated with adverse outcomes, including death. The prevalence and the risk factors for opioid use in IBD are poorly characterized. METHODS: We used the population-based Manitoba IBD Epidemiology Database to identify all individuals in Manitoba with IBD who were prescribed opioids both before and following diagnosis. We determined the point prevalence of any opioid use, as well as the risk of becoming a heavy opioid user (defined as continuous use for 30 days at a dose exceeding 50 mg morphine/day or equivalent). Logistic regression and Cox proportional hazards models were generated to assess whether IBD was an independent risk factor for opioid use, the risk factors for opioid use in individuals with IBD, and to determine whether opioid use was associated with excess mortality in IBD. RESULTS: Within 10 years of diagnosis, 5% of individuals with IBD had become heavy opioid users. Moderate use of opioids before diagnosis was strongly predictive of future heavy use. Individuals with IBD were significantly more likely to become heavy opioid users than their matched controls (odds ratio (OR) 2.91, 95% confidence interval (CI) 2.19-3.85). Heavy opioid use was strongly associated with mortality (OR 2.82, 95% CI 1.58-5.02). CONCLUSIONS: IBD is an independent risk factor for becoming a heavy opioid user, and heavy opioid use is associated with excess mortality in IBD patients. Clinicians should recognize risk factors for future heavy opioid use among their patients with IBD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".