Bibliographic record
Abstract
BACKGROUND: The culmination of the widespread overprescription of opioids, a resurgence of heroin use, and increased accessibility and use of illicit synthetic opioids is commonly referred to as the opioid epidemic in North America. METHODS: This article is not intended to provide a comprehensive systematic literature review, but rather summarized recent publications and online governmental reports and datasets for English-written literature primarily published between January 1, 2015 and July 1, 2020. RESULTS: In both the United States and Canada, opioids represent one of the most widely prescribed classes of medications. According to the US Centers for Disease Control and Prevention (CDC), an unprecedented increase in the use of opioid pain relievers has led to one of the worst drug overdose epidemics in US history and continues to be an ongoing major public health crisis based on recent Centers for Disease Control and Prevention mortality data, where almost two-thirds of all overdose deaths still involve opioids, including heroin and illicit opioids. In addition to the high mortality rates in both the United States and Canada, there has also been an increase in emergency department visits for nonmedical use of opioid pain relievers, along with additional individuals seeking treatment for opioid addiction, and a rise in neonatal abstinence syndrome. CONCLUSIONS: This article highlights the history, underlying issues, ongoing national regulatory efforts, and future strategies and therapies to help mitigate the opioid crisis in North America.
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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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".