Illicit kidney trade networks, operating globally, involve intricate interactions among various players, most notably buyers, sellers, brokers, and surgeons. A comprehensive understanding of these trade networks is, however, hindered by the lack of systematically amassed data for analysis. Further, extracting the geographic locations of buyers, sellers, brokers, transplant surgeons, and medical facilities in all relevant publications often involves extensive, time-consuming, manual labelling that is very costly. Although current techniques such as Named Entity Recognition (NER) tools can potentially automate the process, they are limited to identifying country names and often fail to associate the roles (i.e., offering buyer, seller, broker and/or surgery) that each country played. This study employed state-of-the-art technologies, including Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformers (GPT) model Llama3.3 from Meta in developing a kidney trade country database. We first extracted news articles reporting illicit kidney trade from the LexisNexis database (2000–2022). BERT and Llama3.3 with chain-of-thought prompt tuning strategies were then applied to the materials to determine the relevance of articles to the illegal kidney trade and to identify the roles those different countries played in kidney trade cases over the past 23 years. The specific country classes recorded in the final kidney trade database included: a) countries of origin for kidney sellers; b) countries of origin of kidney buyers; c) countries performing illegal transplant surgeries; and d) countries of origin of organ trafficking brokers. The BERT classification model achieved an accuracy of 88.75

Background:The rapid emergence of artificial intelligence-based large language models (LLMs) in 2022 has initiated extensive discussions within the academic community. While proponents highlight LLMs' potential to improve writing and analytical tasks, critics caution against the ethical and cultural implications of widespread reliance on these models. Existing literature has explored various aspects of LLMs, including their integration, performance, and utility, yet there is a gap in understanding the nature of these discussions and how public perception contrasts with expert opinion in the field of public health. Objective:This study sought to explore how the general public's views and sentiments regarding LLMs, using OpenAI's ChatGPT as an example, differ from those of academic researchers and experts in the field, with the goal of gaining a more comprehensive understanding of the future role of LLMs in health care. Methods:We used a hybrid sentiment analysis approach, integrating the Syuzhet package in R (R Core Team) with GPT-3.5, achieving an 84% accuracy rate in sentiment classification. Also, structural topic modeling was applied to identify and analyze 8 key discussion topics, capturing both optimistic and critical perspectives on LLMs. Results:Findings revealed a predominantly positive sentiment toward LLM integration in health care, particularly in areas such as patient care and clinical decision-making. However, concerns were raised regarding their suitability for mental health support and patient communication, highlighting potential limitations and ethical challenges. Conclusions:This study underscores the transformative potential of LLMs in public health while emphasizing the need to address ethical and practical concerns. By comparing public discourse with academic perspectives, our findings contribute to the ongoing scholarly debate on the opportunities and risks associated with LLM adoption in health care.

INTRODUCTION:Atrial fibrillation (AF) in end-stage kidney disease (ESKD) and kidney transplant (KTx) recipients presents challenges in stroke risk management. This study aimed to compare hospitalization rates for ischemic and hemorrhagic cerebrovascular events in ESKD and KTx patients with and without AF. METHODS:Using the National Inpatient Sample (2005-2019), retrospective analysis was conducted on hospitalizations for ESKD and KTx patients with and without AF. Baseline characteristics and hospitalization rates for five cerebral ischemic conditions and one hemorrhagic condition were compared. Descriptive statistics and t-tests were employed for analysis. RESULTS:Among ESKD patients, those with AF exhibited significantly higher hospitalization rates for ischemic stroke, including 1)Cerebral infarction due to thrombosis, embolism, occlusion (0.11% vs. 0.08%,p<0.001), 2)Cerebral infarction due to thrombosis, embolism, and unspecified occlusion (1.93% vs. 1.51%, p<0.001), 3)Artery occlusion resulting in cerebral ischemia (1.37% vs. 0.93%,p<0.001), 4)Cerebral artery occlusion resulting in cerebral ischemia (0.48% vs. 0.42%,p<0.001), while experiencing lower rates of intraoperative and postprocedural cerebrovascular infarction (0.88% vs. 0.97%,p<0.001) compared to those without AF. Conversely, KTx patients with AF showed increased hospitalizations for hemorrhagic stroke, particularly nontraumatic intracranial hemorrhage (0.79% vs. 0.56%,p<0.001), compared to those without AF. However, they did not exhibit significant differences in hospitalization rates for most ischemic conditions, except for cerebral infarction due to thrombosis, embolism, and unspecific occlusion (1.62% vs. 1.11%,p<0.001) and artery occlusion resulting in cerebral ischemia (0.84% vs. 0.52%,p<0.001). CONCLUSION:Our findings reveal patterns in hospitalization rates between ESKD and KTx patients with AF compared to those without AF, with ESKD patients with AF exhibiting higher rates of ischemic stroke compared to ESKD patients without AF and KTx patients with AF showing increased hospitalizations for hemorrhagic stroke compared to those without AF. These findings demonstrate the impact of AF on hospitalization rates for ischemic and hemorrhagic cerebrovascular events in both ESKD and KTx patients.

Individuals with end-stage kidney disease (ESKD) face higher cerebrovascular risk. Yet, the impact of peripheral vascular disease (PVD) and kidney transplantation (KTx) on hospitalization rates for cerebral infarction and hemorrhage remains underexplored. Analyzing 2,713,194 ESKD hospitalizations (2005–2019) using the National Inpatient Sample, we investigated hospitalization rates for ischemic and hemorrhagic cerebrovascular diseases concerning ESKD, PVD, KTx, or their combinations. Patients hospitalized with cerebral infarction due to thrombosis/embolism/occlusion (CITO) or artery occlusion resulting in cerebral ischemia (AOSI) had higher rates of comorbid ESKD and PVD (4.17% and 7.29%, respectively) versus non-CITO or AOSI hospitalizations (2.34%, p < 0.001; 2.29%, p < 0.001). Conversely, patients hospitalized with nontraumatic intracranial hemorrhage (NIH) had significantly lower rates of ESKD and PVD (1.64%) compared to non-NIH hospitalizations (2.34%, p < 0.001). Furthermore, hospitalizations for CITO or AOSI exhibited higher rates of KTx and PVD (0.17%, 0.09%, respectively) compared to non-CITO or AOSI hospitalizations (0.05%, p = 0.033; 0.05%, p = 0.002). Patients hospitalized with NIH showed similar rates of KTx and PVD (0.04%) versus non-NIH hospitalizations (0.05%, p = 0.34). This nationwide analysis reveals that PVD in ESKD patients is associated with increased hospitalization rates with cerebral ischemic events and reduced NIH events. Among KTx recipients, PVD correlated with increased hospitalizations for ischemic events, without affecting NIH. This highlights management concerns for patients with KTx and PVD.

The advent of AI-based large language models (LLMs) in 2022 has given rise to a plethora of discussions within the academic community. The discourse is multifaceted, with enthusiastic users lauding the sophisticated chatbots' potential to assist with writing tasks. Nevertheless, critics have warned that the cultural and ethical implications of relying on LLMs may be too costly to bear. The literature on LLMs spans multiple fields and often focuses on overlapping themes, such as their appropriate integration, analytical performance, and practical benefits for users. However, there is a notable gap in examining the nature of these discussions. The societal impact of LLMs ultimately depends on users' opinions, as technophiles and luddites shape the trajectory of technological adoption. To address this, our study assessed public opinion and perception of the most popular LLM available: ChatGPT. In our current work, we aimed to understand how LLMs opinions and sentiment shared by the general public may contrast the opinions shared among academic researchers and other field experts to gain a broader view for the future direction of LLMs in healthcare. We utilized the Academic Twitter API to retrieve tweets with search terms “ChatGPT AND (health OR healthcare OR hospital OR physician OR nurse OR nursing OR patient)”. This data collection process was executed for the period between December 1st, 2022, the day after ChatGPT became publicly available, to March 20th, 2023. Our analysis consisted of three phases: 1) Human-labeled sentiment tweet classification; 2) Algorithm-based sentiment tweet classification; and 3) Structural Topic Model to distinctly group tweet content. Using an innovative approach that integrates the Syuzhet package with GPT-3.5, we achieved 84% accuracy in sentiment classification. Further investigation using structural topic modeling revealed eight distinct topics covering both optimistic and concerned perspectives. The results indicated a predominantly positive sentiment towards the integration of LLMs in healthcare, especially in areas such as patient care and decision making. However, notable concerns were raised in the areas of mental health support and patient communication. This study highlights the significant potential of LLMs to transform healthcare, while also addressing the ethical and practical challenges. It further contributes to the ongoing scholarly discourse concerning the advantages and disadvantages of LLMs within the healthcare domain.

Background: Patients with end-stage renal disease (ESRD) face a higher risk of cerebrovascular disease, but the impact of peripheral vascular disease (PVD) and a kidney transplant (KiTx) on hospitalization rates for cerebrovascular conditions remains uncertain. This study aimed to investigate the rates of hospitalization for cerebrovascular disease in patients with ESRD, PVD, KiTx, or a combination. Methods: Using the National Inpatient Sample (NIS), 2,713,194 ESRD hospitalizations (2005-2019) were analyzed. We focused on two cerebrovascular conditions of interest: 1) cerebral infarction due to thrombosis, embolism, and occlusion (CITO) and 2) artery occlusion resulting in cerebral ischemia (AOSI). Comparative analysis utilized t-tests and odds ratios. Results: Among ESRD hospitalizations, 66,927 (2.47%) had KiTx, 63,411 (2.34%) had PVD, and 1,406 (0.05%) had both. Key findings included: 1) ESRD patients with PVD had higher odds of hospitalization for CITO (OR 1.33, CI 1.1-1.8, p=0.009) or AOSI (OR 2.63, CI 2.5-2.8, p<0.001) compared to those without PVD (Fig. 1AB); 2) ESRD patients hospitalized with CITO had higher odds of having KiTx and PVD compared to those without KiTx or PVD (OR 3.17, CI 1.0-9.3, p=0.035, Fig. 1AB); 3) ESRD patients with PVD had higher rates of being hospitalized with AOSI (7.29%) than those without AOSI (2.29%, p<0.001); 4) ESRD patients hospitalized with CITO had higher odds of having KiTx and PVD compared to those without KiTx or PVD (OR 3.17, CI 1.0-9.3, p=0.035, Fig. 1AB) and 5) ESRD patients hospitalized with AOSI demonstrated a higher rate of having KiTx and PVD (0.09%) compared to those without AOSI (0.05%, p=0.002). Conclusion: This nationwide analysis reveals that the presence of PVD and KiTx is associated with increased rates of hospitalization for cerebrovascular conditions. These findings underscore the importance of proactive management to mitigate the risk of cerebrovascular disease in this vulnerable patient population.

The issuance of disaster declarations has become a politicized matter. Prior research has demonstrated that presidents are more generous in awarding disaster relief in federal election years, and that there is a prevalence to award governors from the opposing political party. Additionally, voters tend to reward presidents seeking re-election to a greater degree for disaster response assistance rather than funding preparedness. The original research for this paper explores the impact of natural disasters on re-election rates and analyzes voter trends during presidential election years in Federal Emergency Management Agency (FEMA) Region 3 states for congruence with existing literature covering a national scope. Evaluations of the behaviors and (re)election margins of Presidents Bush and Obama are explored, and implications for President Trump's re-election effort are based on quantitative data and qualitative comparisons.

This article applies Social Network Analysis (SNA) to understand preliminarily the relation between different actors that communicate in social media platforms (essentially through Twitter), report situations of risk, and inform about matters of organized crime, violence and insecurity in the Mexican state of Tamaulipas. This analysis finds a close relationship between law enforcement agents, state and local politicians, local and national reporters, “citizen journalists”, as well as key anonymous social media users that represent a variety of interests—including possibly those of corrupt authorities and even organized crime. The present study highlights the preponderance of anonymous accounts when reporting about organized crime in Tamaulipas.