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Enregistrement W3120505594 · doi:10.1111/add.15376

It is time to recognize that synthetic opioids are not going away

2021· letter· en· W3120505594 sur OpenAlexaboutno aff
Amy S. B. Bohnert, Lewei Lin

Notice bibliographique

RevueAddiction · 2021
Typeletter
Langueen
DomaineMedicine
ThématiqueOpioid Use Disorder Treatment
Établissements canadiensnon disponible
Organismes subventionnairesU.S. Department of Veterans Affairs
Mots-clésFentanyl(+)-NaloxoneHeroinMedicineOpioid overdoseMedical prescriptionOpioidPsychological interventionAddictionIntensive care medicineDrugAnesthesiaPsychiatryPharmacology

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,121
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0130,012

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,023
Tête enseignante GPT0,254
Écart entre enseignants0,231 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2021
Routes d'admission1
Résumé présentoui

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