Persistence of use of prescribed cannabinoid medicines in Manitoba, Canada: a population‐based cohort study
Bibliographic record
Abstract
BACKGROUND AND AIMS: To estimate prevalence of continuous use (persistence) of prescribed cannabinoid medications for up to 1 year from initial prescription in Manitoba, Canada and predictors of duration of use. DESIGN AND SETTING: A retrospective, population-based, cohort study using administrative data from the Manitoba Population Research Data Repository located at the Manitoba Centre for Health Policy, Canada. PARTICIPANTS: People without a record of a previous prescription who were prescribed a cannabinoid medication from 1 April 2004 to 1 April 2016 followed for 1 year from the date of first prescription. MEASUREMENTS: Continuous prescribed cannabinoid medication use was defined as use without a gap exceeding 60 days between prescriptions. The primary outcome was prevalence of continuous prescribed cannabinoid medication use for up to 1 year. A secondary outcome was duration of continuous use. Predictors were socio-demographic characteristics, medical diagnoses and type of cannabinoid medication. FINDINGS: Among 5452 new users, 18.1% [95% confidence interval (CI) = 17.08-19.12] were still using cannabinoids at 1 year. Median duration of use was 31 days [interquartile range (IQR) = 25-193]. This was highest for nabilone (33 days, IQR = 25-199) and lowest for nabiximols (20 days, IQR = 7-30). Use was longest among 19-45- and 46-64-year-old users and those with the highest socio-economic status. Fibromyalgia [hazard ratio (HR) = 0.89, 95% CI = 0.84-0.95], osteoarthritis (HR = 0.91, 95% CI = 0.82-0.97) and substance use disorder (HR = 0.85, 95% CI = 0.76-0.94) diagnoses were associated with longer use (HR for discontinuation-HR < 1 less discontinuation and longer use). A diagnosis of cancer was associated with shorter use (HR = 2.73, 95% CI = 2.02-3.67). CONCLUSIONS: In Manitoba, Canada approximately 18% of people prescribed cannabinoid medication continue using for at least 1 year. Duration of use varies with type of cannabinoid medication, age, socio-economic status and dagnosis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".