“Nonmedical” prescription opioid use in North America: a call for priority action
Bibliographic record
Abstract
Nearly four years after the United States Congress heralded a "decade of pain control and research", chronic pain remains a mounting public health concern worldwide. The escalating prevalence of chronic pain in recent years has been paralleled by a rise in prescription opioid availability, misuse, and associated human and social costs. However, national monitoring surveys in the U.S. and Canada currently fail to differentiate between prescription opioid misuse for the purposes of euphoria versus pain or withdrawal management. Furthermore, there is a lack of evidence-based guidelines for pain management among high-risk individuals, and a glaring lack of education for practitioners in the areas of pain and addiction medicine. Herein we propose multiple avenues for intervention and research in order to mitigate the individual, social and structural problems related to undertreated pain and prescription opioid misuse.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".