It is time to recognize that synthetic opioids are not going away
Bibliographic record
Abstract
Addiction policy, research and treatment has largely treated the problems of fentanyl and synthetic opioids as a temporary crisis. Six years later, and with all signs pointing to continued spread of fentanyl in drug markets across North America, innovative approaches are needed. The article by Pardo and colleagues provides a novel discussion of the development and persistence of fentanyl and other synthetic opioid markets in a number of European countries and the United States and Canada [1]. In contrast to prior outbreaks, the current North American fentanyl surge shows no signs of waning. The idea that fentanyl is here to stay in North American drug markets seems little considered in policy, research and treatment circles, and the authors effectively draw attention to the problems caused by this oversight. As an example, there has been very little effort to adapt prior overdose prevention interventions or develop novel technology to address fentanyl risks specifically, despite the known differences between synthetic opioids, prescription opioids and heroin. The high dose amounts of fentanyl and other synthetic opioid overdoses can require multiple doses of naloxone [2], in some cases threatening stability of hospital naloxone supply when synthetics first enter a local drug market. However, innovations in overdose reversal drug development have seemed to have stalled after the early 2010s brought novel delivery methods, such as a nasal spray and auto-injector, geared towards addressing barriers for oral prescription opioid users. Furthermore, the very few fentanyl-specific interventions are based on the premise that no one is taking fentanyl intentionally. A key example are the programs to distribute fentanyl test strips to people who use street opioids so that the person given the strips can test a supply of heroin and discard the drugs if they turn out to have been contaminated with, or fully replaced by, fentanyl [3]. The idea that the vast majority of people who use opioids would be seeking to avoid use of fentanyl may have been true when fentanyl first entered the US heroin markets. However, a 2017 survey of people who use opioids in three East coast cities found that 27% endorsed the statement ‘I prefer drugs with fentanyl in them’ [4]. Further, in many locations where fentanyl has been in the drug market for several years, finding heroin not contaminated with synthetic opioids is no longer an option. Additionally, the issue of concurrent use of fentanyl and other substances needs more consideration in research. This is particularly true for the impact of fentanyl combined with cocaine and other stimulants, which poses unique challenges for both overdose prevention and addiction treatment. Although stimulants are the cause of fewer overdose deaths than fentanyl and other opioids, the evidence base for prevention and treatment is even more sparse. There are many unanswered questions in this area, including how treatment should be tailored for the heterogeneous group of patients who use stimulants and fentanyl, some whom may have underlying stimulant and opioid use disorders and others who primarily have an addiction to one substance or the other. Currently, there is concern that patients with underlying opioid use disorder who use other substances, including stimulants, may be less likely to receive medication treatment [5], even though this group may actually be more prone to overdose. At the same time, there is minimal knowledge about the effectiveness of standard medication treatments, namely buprenorphine, methadone and extended-release naltrexone, for patients with synthetic opioid use. There have been very few studies, including either randomized controlled trials or studies using secondary data, examining the efficacy of these medications in the synthetic opioid-using patient population [6]. Although treatment outcomes may be similar [7], there is reason to be concerned that dosing may need to be tailored for patients who are primarily using synthetic opioids, given the differences in potency. It is also unclear how the three medications compare for this patient population. Patients who use fentanyl can report a higher likelihood of precipitated withdrawals and more difficult experiences with buprenorphine induction compared to patients who use heroin or prescription opioids [8], which may be mitigated by different dosing strategies, but research in this area is also sparse. Thus, the authors’ call for new innovations that address the unique challenges of synthetic opioids is particularly critical, although this must be balanced with the fundamental need to improve all addiction prevention and treatment collectively. As synthetic opioid-related mortality increases in the western United States [9], we can no longer ignore that the fentanyl market is persisting, and need to prioritize research and funding to address this problem. L. A. L. is a Faculty Expert on alcohol use disorder for the National Committee for Quality Assurance with funding through a grant by Alkermes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.012 |
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; both teacher heads agree on what is shown here.
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".