Jeanmarie Perrone

Introduction: Nitazenes are an emerging class of highly potent synthetic opioids not approved for clinical use. Knowledge of nitazene-associated practices and clinical effects remains limited. Reddit contains nitazene-related discussions describing self-reported experiences. We analyzed nitazene mentions to characterize these experiences. Methods: Nitazene-mentioning posts between 01/2019 and 10/2025 were extracted via natural language processing-based lexical filters, and a random sample of 293 mentions was qualitatively reviewed and annotated for themes consolidated via consensus discussion. Repeated findings more specific than themes were synthesized as subthemes, and a validated GPT-4o-based pipeline was applied to all mentions to forecast the theme distribution. Results: A total of 31,638 mentions were extracted; frequency increased between 2022 and 2024 (mean 584 mentions/month, P <0.01 increase compared with 2019–2021’s mean of 197 mentions/month). Ten themes were curated, with source (31.3%), potency and dose (26.5%), and uncertainty (25.9%) most prevalent in the annotated sample. Across the full dataset, automated characterization predicted that general experience (44.7%), general comment (37.9%), and potency and dose (27.6%) were the most prevalent thematic labels. Nine subthemes included information about counterfeit pills, withdrawal management difficulties, motivating factors for use, benzodiazepine adulteration, and vascular injury associated with intravenous administration. Discussion: Increasing nitazene mentions on Reddit may reflect increasing interest and use. Thematic characterization identified patterns in nitazene use, effects, and distribution relevant to clinical practice and public health. Insights provided by subthemes align with laboratory potency assessments and emerging evidence from case series and health alerts. Conclusion: These findings can inform public health monitoring, overdose prevention, and clinical characterizations of nitazene use.

Purpose of Review: Medetomidine is a veterinary α2-adrenergic agonist that has been an adulterant of illicitly manufactured opioids since 2022. We review its pharmacology, detection, intoxication, withdrawal, and management, and assess why severe withdrawal has emerged in some regions but not others. Recent Findings: Medetomidine has spread through North American drug markets yet evades most toxicology testing. Roughly two hundred times more potent than xylazine, with greater α2 selectivity and imidazoline-1 activity, it causes intoxication resembling other α2 agonists: sedation and bradycardia. The dominant clinical problem is withdrawal: autonomic dysregulation, intractable vomiting, and encephalopathy frequently requiring intensive care. Treatment requires multimodal α2-agonist therapy, often escalating to parenteral dexmedetomidine. Where it is established, withdrawal encounters and hospital admissions have risen. Summary: We hypothesize that both local prevalence and drug concentration determine where withdrawal appears. This syndrome exposes gaps in both assessment and treatment; prospective multicenter research, a validated withdrawal instrument, and integrated addiction-critical care models are needed.

Context: Not-for-profit hospitals are required to provide community benefits (CB) in exchange for tax-exempt status, yet CB spending remains the predominant measure for assessing whether hospitals meet these obligations. Newly available hospital price transparency data offer an opportunity to evaluate whether hospitals' stated CB health priorities align with their financial practices. Objective: To examine whether not-for-profit hospitals that prioritize opioid use disorder (OUD) in CB planning are more likely to comply with price reporting for medications commonly prescribed for OUD treatment (OUD medications) and offer lower self-pay prices for OUD medications. Design: Mixed-methods cross-sectional study. Setting: United States not-for-profit hospitals. Participants: A nationally representative sample of 534 not-for-profit hospitals. Main Outcome Measure(s): Hospital compliance with OUD medication price reporting and median self-pay prices for medications commonly prescribed for OUD treatment. Results: Hospitals that prioritized OUD (n = 217; 40.6%) were located in counties with higher overdose mortality and greater levels of poor mental health. However, hospitals that prioritized OUD were neither significantly more likely to report self-pay prices for OUD medications nor more likely to offer lower self-pay prices for these medications. Overall, we found limited evidence that hospitals identifying OUD as a community health priority translated these stated priorities into pricing practices that may improve affordability for uninsured and underinsured patients. Conclusions: Identifying OUD as a community health priority may not be sufficient to ensure alignment between hospitals' stated commitments and financial practices affecting affordability and access to treatment. As calls for stronger hospital accountability grow, integrating price transparency data into CB oversight may help policymakers and hospital leaders strengthen accountability, transparency, and alignment between community health priorities and patient financial protections.

BACKGROUND Chronic pain poses a significant global health burden, with pertinent contextual information relevant to it encapsulated in free text format across sources such as electronic health records (EHRs), published literature, and social media. Natural language processing (NLP), including recent advances in large language models (LLMs), presents a transformative opportunity to analyze this unstructured data, but the literature is fragmented across disciplines, and there is a need to consolidate existing knowledge, identify gaps in the literature, and inform future research directions in this emerging field. OBJECTIVE This review aims to investigate and characterize NLP-based methods designed for chronic pain research. METHODS A search strategy was formulated and executed across PubMed, Web of Science, IEEE Xplore, Scopus, and ACL Anthology to find studies published in English between 2014 and 2025. Included studies were characterized in terms of study design, research question, dataset leveraged, NLP method(s) employed, number of participants included, participant age-groups, and key results. Potential reporting bias and missing results were also analyzed. RESULTS After screening 155 papers, 34 studies were included in the final review. We observed a noticeable trend toward transformer-based models and LLMs in recent years. A slight majority of studies (13/34) relied on EHR data. Earlier studies commonly used methods such as logistic regression, support vector machines, Latent Dirichlet Allocation (LDA), and word embeddings. In contrast, recent work has shifted toward transformer-based models (e.g., BERT, RoBERTa, BioBERT) and general-purpose LLMs like GPT-3.5 for zero- or few-shot learning frameworks, generally outperforming traditional approaches in supervised learning tasks (e.g., classification F1 > 0.8). Despite progress, persistent challenges remain, including small and nonrepresentative datasets, limited attention to underrepresented populations, and a lack of cross-disciplinary standardization. CONCLUSIONS While application of NLP for chronic pain research is promising, the review revealed a paucity of research on the topic, with opportunities for future explorations. Future work may focus on development and validation of generalizable approaches involving diverse data and cohorts, multimodal data validation systems, public release of data and models, and the development of standardized evaluation metrics to enhance reproducibility and equity in chronic pain research.

Background: Xylazine has been associated with skin wounds. The rising prevalence of xylazine and its debated role in wound causation have sparked concerns among public health professionals, medical experts, and people who use drugs. Objective: This study used a qualitative evaluation of Reddit posts to understand the experiences of people who use drugs concerning xylazine-associated wounds. Methods: This study explored xylazine discussions on Reddit. Data were collected from 930+ drug-related subreddits via the PRAW Python application programming interface, and natural language processing methods were employed to identify posts that mentioned xylazine and wound-related keywords. Retrieved posts were manually coded for thematic analysis, and a term frequency–inverse document frequency analysis was performed per theme to obtain additional insights. Results: The manual classification of 286 posts revealed predominant themes related to the pathophysiology of xylazine, wound locations on the body, and management strategies. The 3 most frequent xylazine wound-related themes were “Mechanisms of xylazine-associated wounds” (84 posts, 29.4%), “Geographic region” (67, 23.4%), and “Location of wounds on the body” (56, 19.6%). The analysis showed xylazine’s presence in the discussions among Reddit’s drug-using communities, with a notable focus on wound management and geographic trends. The term frequency–inverse document frequency analysis revealed prominent lexical markers within each theme. Conclusions: The findings suggest that social media platforms such as Reddit can serve as valuable resources for understanding emerging health issues such as xylazine-associated wounds. The study’s findings highlight patterns of use, the characteristics of wounds on people who use drugs, and discussions about wound management. This study adds to a growing body of literature using social media to understand the consequences of emerging drugs on human health.

Background Large language models (LLMs) face challenges in inductive thematic analysis, a task requiring deep interpretive and domain-specific expertise. We evaluated the feasibility of using LLMs to replicate expert-driven thematic analysis of social media data. Methods Using two temporally non-intersecting Reddit datasets on xylazine (n=286 and n=686, for model optimization and validation, respectively) with twelve expert-derived themes, we evaluated five LLMs against expert coding. We modeled the task as a series of binary classifications, rather than a single, multi-label classification, employing zero-, single-, and few-shot prompting strategies and measuring performance via accuracy, precision, recall, and F1-score. Results On the validation set, GPT-4o with two-shot prompting performed best (accuracy: 90.9%; F1-score: 0.71). For high-prevalence themes, model-derived thematic distributions closely mirrored expert classifications (e.g., xylazine use: 13.6% vs. 17.8%; MOUD use: 16.5% vs. 17.8%). Conclusions Our findings suggest that few-shot LLM-based approaches can automate thematic analyses, offering a scalable supplement for qualitative research. Keywords: thematic analysis, large language models, natural language processing, qualitative analysis, social media, prompt engineering, public health

Background: The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging. Objective: This paper aims to develop a retrieval-augmented generation (RAG) architecture for medical question answering pertaining to clinicians’ queries on emerging issues associated with health-related topics, using user-generated medical information on social media. Methods: We proposed a two-layer RAG framework for query-focused answer generation and evaluated a proof of concept for the framework in the context of query-focused summary generation from social media forums, focusing on emerging drug-related information. Our modular framework generates individual summaries followed by an aggregated summary to answer medical queries from large amounts of user-generated social media data in an efficient manner. We compared the performance of a quantized large language model (Nous-Hermes-2-7B-DPO), deployable in low-resource settings, with GPT-4. For this proof-of-concept study, we used user-generated data from Reddit to answer clinicians’ questions on the use of xylazine and ketamine. Results: Our framework achieves comparable median scores in terms of relevance, length, hallucination, coverage, and coherence when evaluated using GPT-4 and Nous-Hermes-2-7B-DPO, evaluated for 20 queries with 76 samples. There was no statistically significant difference between GPT-4 and Nous-Hermes-2-7B-DPO for coverage (Mann-Whitney U=733.0; n1=37; n2=39; P=.89 two-tailed), coherence (U=670.0; n1=37; n2=39; P=.49 two-tailed), relevance (U=662.0; n1=37; n2=39; P=.15 two-tailed), length (U=672.0; n1=37; n2=39; P=.55 two-tailed), and hallucination (U=859.0; n1=37; n2=39; P=.01 two-tailed). A statistically significant difference was noted for the Coleman-Liau Index (U=307.5; n1=20; n2=16; P<.001 two-tailed). Conclusions: Our RAG framework can effectively answer medical questions about targeted topics and can be deployed in resource-constrained settings.

Objectives Xylazine is an alpha-2 agonist increasingly prevalent in the illicit drug supply. Our objectives were to curate information about xylazine through social media from People Who Use Drugs (PWUDs). Specifically, we sought to answer the following: 1) what are the demographics of Reddit subscribers reporting exposure to xylazine? 2) is xylazine a desired additive? and 3) what adverse effects of xylazine are PWUDs experiencing? Methods Natural Language Processing (NLP) was used to identify mentions of “xylazine” from posts by Reddit subscribers who also posted on drug-related subreddits. Posts were qualitatively evaluated for xylazine-related themes. A survey was developed to gather additional information about the Reddit subscribers. This survey was posted on subreddits that were identified by NLP to contain xylazine-related discussions from March 2022 to October 2022. Results 76 posts mentioning xylazine were extracted via NLP from 765,616 posts by 16,131 Reddit subscribers (January 2018 to August 2021). People on Reddit described xylazine as an unwanted adulterant in their opioid supply. 61 participants completed the survey. Of those that disclosed their location, 25/50 (50%) participants reported locations in the Northeastern United States. The most common eoute of xylazine use was intranasal use (57%). 31/59 (53%) reported experiencing xylazine withdrawal. Frequent adverse events reported were prolonged sedation (81%) and increased skin wounds (43%). Conclusions Among respondents on these Reddit forums, xylazine appears to be an unwanted adulterant. PWUDs may be experiencing adverse effects such as prolonged sedation and xylazine withdrawal. This appeared to be more common in the Northeast.