Prescription Opioids, Opioid Use Disorder, and Overdose Crisis in Canada: Current Dilemmas and Remaining Questions
Bibliographic record
Abstract
ABSTRACT In Canada, a rise in opioid use disorder (OUD) and overdose has been linked to opioid prescriptions in a number of contexts. At the same time, relatively few patients prescribed opioids reportedly develop OUD. This combination of findings suggests a pressing need for research on specific avenues through which medically prescribed opioids influence OUD and overdose in Canada. In this commentary, we therefore discuss a few of the potential processes that might allow for medically prescribed opioids to indirectly influence rising overdose rates, and the processes that might account for inconsistencies between large correlational research and studies of OUD incidence in opioid-prescribed patients. Au Canada, une augmentation du trouble de l’usage des opioïdes (OUD) et la surdose ont été associées aux prescriptions d’opioïdes dans un certain nombre de contextes. Dans le même temps, relativement peu de patients qui se sont fait prescrire des opioïdes ont développés une OUD. Cette combinaison de résultats suggère un besoin pressant de recherche sur des avenues spécifiques par lesquelles les opioïdes prescrits par un médecin (MPO) influencent le DIU et l’overdose au Canada. Dans ces observations, nous discutons quelques-uns des processus potentiels qui pourraient permettre aux MPO d’influencer indirectement les taux de surdose croissants, et les processus qui pourraient expliquer les incohérences entre les grandes recherches corrélationnelles et les études d’incidence OUD chez les patients opioïdes.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".