Trends in high-dose opioid prescribing in Canada.
Bibliographic record
Abstract
OBJECTIVE: To describe trends in rates of prescribing of high-dose opioid formulations and variations in opioid product selection across Canada. DESIGN: Population-based, cross-sectional study. SETTING: Canada. PARTICIPANTS: Retail pharmacies dispensing opioids between January 1, 2006, and December 31, 2011. MAIN OUTCOME MEASURES: Opioid dispensing rates, reported as the number of units dispensed per 1000 population, stratified by province and opioid type. RESULTS: The rate of dispensing high-dose opioid formulations increased 23.0%, from 781 units per 1000 population in 2006 to 961 units per 1000 population in 2011. Although these rates remained relatively stable in Alberta (6.3% increase) and British Columbia (8.4% increase), rates in Newfoundland and Labrador (84.7% increase) and Saskatchewan (54.0% increase) rose substantially. Ontario exhibited the highest annual rate of high-dose oxycodone and fentanyl dispensing (756 tablets and 112 patches per 1000 population, respectively), while Alberta's rate of high-dose morphine dispensing was the highest in Canada (347 units per 1000 population). Two of the highest rates of high-dose hydromorphone dispensing were found in Saskatchewan and Nova Scotia (258 and 369 units per 1000 population, respectively). Conversely, Quebec had the lowest rate of high-dose oxycodone and morphine dispensing (98 and 53 units per 1000 population, respectively). CONCLUSION: We found marked interprovincial variation in the dispensing of high-dose opioid formulations in Canada, emphasizing the need to understand the reasons for these differences, and to consider developing a national strategy to address opioid prescribing.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".