Surveillance of Xylazine Use and Poisonings Is Needed—Without Blind Spots
Notice bibliographique
Résumé
Editorials10 October 2023Surveillance of Xylazine Use and Poisonings Is Needed—Without Blind SpotsJoseph J. Palamar, PhD, MPH, Bruce A. Goldberger, PhDJoseph J. Palamar, PhD, MPHDepartment of Population Health, New York University Grossman School of Medicine; New York, New York, Bruce A. Goldberger, PhDDepartment of Pathology, Immunology and Laboratory Medicine, University of Florida College of Medicine; Gainesville, FloridaAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M23-2299 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Xylazine has become a new addition to the quickly evolving drug landscape in the United States. Just as momentum was gained in testing for fentanyl and other new psychoactive substances and the availability of naloxone to treat fentanyl overdoses increased, fentanyl adulterated with xylazine, a legal veterinary tranquilizer, is now further complicating the opioid crisis. Although it appears that xylazine on its own rarely causes death (1, 2), exposure to xylazine mixed into illicitly manufactured fentanyl has been associated with prolonged sedation (not reversible with naloxone) and a steep increase in deaths (1–3). A new narrative review by D'Orazio and ...References1. Quijano T, Crowell J, Eggert K, et al. Xylazine in the drug supply: emerging threats and lessons learned in areas with high levels of adulteration. Int J Drug Policy. 2023;120:104154. [PMID: 37574646] doi:10.1016/j.drugpo.2023.104154 CrossrefMedlineGoogle Scholar2. Canadian Centre on Substance Use and Addiction (CCSA). An Update on Xylazine in the Unregulated Drug Supply: Harms and Public Health Responses in Canada and the United States. July 2023. Google Scholar3. Kariisa M, O'Donnell J, Kumar S, et al. Illicitly manufactured fentanyl-involved overdose deaths with detected xylazine - United States, January 2019-June 2022. MMWR Morb Mortal Wkly Rep. 2023;72:721-727. [PMID: 37384558] doi:10.15585/mmwr.mm7226a4 CrossrefMedlineGoogle Scholar4. D'Orazio J, Nelson L, Perrone J, et al. Xylazine adulteration of the heroin–fentanyl drug supply. A narrative review. Ann Intern Med. 10 October 2023. [Epub ahead of print]. doi:10.7326/M23-2001 LinkGoogle Scholar5. Cottler LB, Goldberger BA, Nixon SJ, et al. Introducing NIDA's new National Drug Early Warning System. Drug Alcohol Depend. 2020;217:108286. [PMID: 32979739] doi:10.1016/j.drugalcdep.2020.108286 CrossrefMedlineGoogle Scholar6. Love JS, Levine M, Aldy K, et al. Opioid overdoses involving xylazine in emergency department patients: a multicenter study. Clin Toxicol (Phila). 2023;61:173-180. [PMID: 37014353] doi:10.1080/15563650.2022.2159427 CrossrefMedlineGoogle Scholar7. Palamar JJ, Salomone A, Keyes KM. Underreporting of drug use among electronic dance music party attendees. Clin Toxicol (Phila). 2021;59:185-192. [PMID: 32644026] doi:10.1080/15563650.2020.1785488 CrossrefMedlineGoogle Scholar8. DiSalvo P, Cooper G, Tsao J, et al. Fentanyl-contaminated cocaine outbreak with laboratory confirmation in New York City in 2019. Am J Emerg Med. 2021;40:103-105. [PMID: 33360606] doi:10.1016/j.ajem.2020.12.002 CrossrefMedlineGoogle Scholar9. Mattson CL, Tanz LJ, Quinn K, et al. Trends and geographic patterns in drug and synthetic opioid overdose deaths - United States, 2013-2019. MMWR Morb Mortal Wkly Rep. 2021;70:202-207. [PMID: 33571180] doi:10.15585/mmwr.mm7006a4 CrossrefMedlineGoogle Scholar10. Rock KL, Lawson AJ, Duffy J, et al. The first drug-related death associated with xylazine use in the UK and Europe. J Forensic Leg Med. 2023;97:102542. [PMID: 37236142] doi:10.1016/j.jflm.2023.102542 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAuthors: Joseph J. Palamar, PhD, MPH; Bruce A. Goldberger, PhDAffiliations: Department of Population Health, New York University Grossman School of Medicine; New York, New YorkDepartment of Pathology, Immunology and Laboratory Medicine, University of Florida College of Medicine; Gainesville, FloridaDisclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M23-2299.Corresponding Author: Joseph J. Palamar, PhD, MPH, Department of Population Health, New York University Grossman School of Medicine, 180 Madison Avenue, Room 1752, New York, NY 10016; e-mail, joseph.palamar@nyulangone.org.This article was published at Annals.org on 10 October 2023. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoXylazine Adulteration of the Heroin–Fentanyl Drug Supply Joseph D'Orazio , Lewis Nelson , Jeanmarie Perrone , Rachel Wightman , and Rachel Haroz Metrics LatestKeywordsDrugsOpioid addictionOpioidsSubstance abuseUlcersWound healing ePublished: 10 October 2023 Copyright & PermissionsCopyright © 2023 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,007 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».