Tramadol dispensing patterns and trends in Canada, 2007‐2016
Bibliographic record
Abstract
PURPOSE: Opioid use and associated mortality and morbidity have substantially increased in Canada, which recent interventions have aimed to reduce. Tramadol is an atypical prescription-only (but unscheduled under Canada's narcotics law) opioid analgesic and not subject to controls for other (eg, strong) opioids. Given experiences in different jurisdictions, tramadol may have been increasingly dispensed as a "substitute" drug during a period with increasingly restrictive controls for other (scheduled) opioids. METHODS: We examined the annual population-level retail dispensing (as a proxy for use) of tramadol and (scheduled) "strong opioids" in Canadian provinces for 2007-2016 based on data from a representative national sample of community pharmacies, covering the majority of episodes of opioid dispensing. Data for both aforementioned formulation categories were converted into defined daily doses (DDD)/1000 population/day and examined descriptively and by segmented regression analyses (to identify significant breakpoints in trends). RESULTS: Tramadol use strongly increased in all provinces until 2009. After 2009, tramadol dispensing levels either decelerated their increase or plateaued; "strong opioid" dispensing levels, in comparison, increased strongly until 2011 and decelerated or decreased for the remaining period. Tramadol was consistently dispensed at lower levels than "strong opioids." CONCLUSIONS: Tramadol and "strong opioids" showed similar (bifurcated) use trends, with initial increases and subsequent inflections, yet reductions in dispensing occurred earlier for tramadol than for "strong opioids" (the latter occurring following with recent interventions). Distinct from experiences with differential opioid control regimes elsewhere, there is no evidence that tramadol figured as a "substitution" drug for increasingly restricted "strong opioids" in Canada.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".