Faculty Opinions recommendation of The fentanyl epidemic and evolution of fentanyl analogs in the united states and the european union.
Bibliographic record
Abstract
BACKGROUND: Since 2013, an unprecedented surge in fentanyl overdose deaths has been caused by heroin laced with illicitly produced fentanyl and/or fentanyl analogs (FAs) sold as heroin. The US Drug Enforcement Agency's National Forensic Laboratory Information System reported a >300% increase in fentanyl encounters from 4697 in 2014 to 14440 in 2015. In 2015, the CDC reported 9580 deaths caused by synthetic opioids, primarily fentanyl, a 72% increase from 2014. The European Monitoring Centre for Drugs and Drug Addiction has also encountered several new FAs in the heroin supply. Counterfeit pharmaceuticals containing mixtures of fentanyl and FAs continue to be a poorly recognized worldwide problem despite the WHO classifying several FAs as a serious threat to public health.CONTENT: This review covers the epidemiology of fentanyl abuse and discusses the clinical practice implications of widespread fentanyl abuse. It includes a historical perspective on the illicit FAs that have appeared in the US and European Union and reviews the methods available to identify FAs and emerging technologies useful for identifying previously undescribed analogs. A compilation of structural and mass spectral data on FAs reported thus far is provided.SUMMARY: Fentanyl and FAs have evolved into a global public health threat. It is important to understand the analytical, clinical, and regulatory efforts underway to assist communities affected by the current fentanyl epidemic.© 2018 American Association for Clinical Chemistry. PMID: 30305277
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.347 | 0.198 |
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".