Prevalence of Opioid Dispensings and Concurrent Gastrointestinal Medications in Quebec
Bibliographic record
Abstract
BACKGROUND: Opioids are frequently prescribed for moderate to severe pain. A side effect of opioid usage is the inhibition of gastrointestinal (GI) motility, known as opioid-induced bowel dysfunction (OBD). OBD is typically treated prophylactically with laxatives and/or acid suppressants. AIM: The present study describes the prevalence of outpatient opioid dispensing, opioid patient demographics, and concomitant dispensing of opioids and GI medications in the Quebec Public Prescription Drug Insurance Plan in 2005. METHODS: Using a retrospective cohort design, opioid dispensings were identified using claims and reimbursement data. Laxative and acid suppressant dispensings were also identified. Concurrent use was defined as having at least one 'GI medication-exposed day' overlapping an 'opioid-exposed day'. RESULTS: More than 11% of the drug plan population was dispensed an opioid in 2005, and dispensings increased with age. Approximately two-thirds of patients who received an opioid were given codeine. Approximately one-third of opioid patients were concomitantly dispensed a GI medication, yet only 2% were dispensed a laxative. CONCLUSIONS: Although the GI side effects of opioids are well known, these side effects appear to increase with age and duration of opioid use. Opioid-related side effects, particularly OBD, should be effectively managed so as not to lead to the cessation of opioid therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".