The opioid crisis in North America: facts and future lessons for Europe
Bibliographic record
Abstract
Over the past two decades, opioid-related hospitalizations and deaths in North America have reached the level of a public health emergency. Initially, the epidemic of opioid misuse was largely driven by pharmaceutical companies and initiated by their spread of misinformation, which led physicians to engage in overzealous prescribing behaviour. This was followed by significant harms as deaths related to overdoses on prescription and illicit opioids rose steadily throughout the 1990s and early 2000s. This review examines the historical context of the opioid crisis in the United States and Canada, the role of physicians, the contributions of the pharmaceutical industry and the evolution of the epidemic in response to the introduction of highly potent synthetic opioids now recognized as the main culprits in opioid overdose and death. This article further explores the evidence surrounding the effectiveness of various treatment strategies and harm-reduction interventions designed to curtail the morbidity and mortality associated with opioid use. Finally, the magnitude of the opioid epidemic in North America is compared to that in European countries. This paper describes the differences in North American and European experiences with opioid overdose and the evidence-based approaches that can be implemented to reduce the mortality and morbidity linked to opioids while simultaneously ensuring adequate pain control for patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".