The Misuse of Prescription Opioids: A Threat for Europe?
Bibliographic record
Abstract
In the the past two decades the medical use of prescription opioids (POs), in particular oxycodone, increased up to 14-fold in the U.S. and Canada. The high consumption of these pain relievers also led to non-medical use and abuse of these substances which in turn resulted in a dramatic increase in the number of PO related fatalities and opioid dependent subjects. In the U.S. POs became the second most prevalent type of abused drug (4.5 million abusers; 1.7% of the population) after marijuana (8 million abusers) with currently 1.9 million (0.7% of the population) people dependent on opioid pain relievers. Pain relief was the leading motive for non-medical use in about 40% of the cases, but about half of non-medical PO users reported non-pain relief motives only, like to get high or to relax. Since 2011, there is a decline in the use and misuse of POs and reduction in painkiller overdose deaths in the U.S. probably due to the introduction of a variety of restrictive regulations. In Europe, the medical use of POs is increasing as well, but at a much slower rate than in the U.S. Moreover, in Europe non-medical use of POs and fatal PO incidents are (still) rare. The paper highlights and discusses the differences between Europe versus U.S. and Canada in an attempt to assess the risk of a PO abuse and overdose epidemic in Europe. It is concluded that the risk in Europe seems to be rather limited but vigilance is needed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".