Characteristics and incidence of opioid analgesic initiations to opioid naïve patients in a Canadian primary care setting
Bibliographic record
Abstract
Objective: To examine characteristics and incidence of opioid analgesic initiations to opioid naïve patients in a Canadian primary care setting. Methods: This is a population-based cross-sectional study, outlining an analysis of health administrative data recorded in a centralized medication monitoring database (PharmaNet) covering 96% of population in British Columbia (BC), Canada. From the PharmaNet database, 5,657 doctors (87% of all practicing family physicians) were selected on the bases of (1) having been currently treating patients (defined as having written at least 25 prescriptions, for any drug, in preceding 12 months); and (2) having prescribed at least one opioid during study period. The primary outcome measure is incidence of new starts for opioid analgesics in opioid naïve people, stratified by several important prescriber and regional characteristics (e.g., graduation year, geographical location). Results: Between December 1 st , 2018 and November 30th , 2019, there were 139,145 opioid initiations to opioid naïve patients. The mean monthly initiation rate was 2.05 prescriptions per physician. Most initiations were in Lower Mainland regions of BC, also where the population is most concentrated (46,456, 33% in the Fraser region), by prescribers who graduated between 1986-1995 (39,601, 28%), and had less than 10 patient visits per day (72,506, 52%). Conclusions: From data presented in this study, it appears that the rate of opioid analgesic initiations in primary care remains unchanged. Individualized prescribing interventions targeted at physicians are urgently needed considering the current opioid epidemic and known links with opioid analgesics that raise concerns about the potential to cause harm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".