Surveillance of Xylazine Use and Poisonings Is Needed—Without Blind Spots
Bibliographic record
Abstract
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 ...
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".