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Background Gastrointestinal stromal tumors (GISTs) represent the most common mesenchymal neoplasms of the gastrointestinal tract, arising from the interstitial cells of Cajal. These tumors bridge the nervous system and muscular layers of the gastrointestinal tract, playing a crucial role in the digestive process. The incidence of GISTs demonstrates notable variations across different racial and ethnic groups, underscoring the need for in-depth analysis to understand the interplay of genetic, environmental, and socioeconomic factors behind these disparities. Linear regression analysis is a pivotal statistical tool in such epidemiological studies, offering insights into the temporal dynamics of disease incidence and the impact of public health interventions. Methodology This investigation employed a detailed dataset from 2009 to 2020, documenting GIST incidences across Asian, African American, Hispanic, and White populations. A meticulous preprocessing routine prepared the dataset for analysis, which involved data cleaning, normalization of racial terminologies, and aggregation by year and race. Linear regression models and Pearson correlation coefficients were applied to analyze trends and correlations in GIST incidences across the different racial groups, emphasizing an understanding of temporal patterns and racial disparities in disease incidence. Results The study analyzed GIST cases among four racial groups, revealing a male predominance (53.19%) and an even distribution of cases across racial categories: Whites (27.66%), Hispanics (25.53%), African Americans (24.47%), and Asians (22.34%). Hypertension was the most common comorbidity (32.98%), followed by heart failure (28.72%). The linear regression analysis for Asians showed a decreasing trend in GIST incidences with a slope of -0.576, an R-squared value of 0.717, and a non-significant p-value of 0.153. A significant increasing trend was observed for Whites, with a slope of 0.581, an R-squared value of 0.971, and a p-value of 0.002. African Americans exhibited a moderate positive slope of 0.277 with an R-squared value of 0.470 and a p-value of 0.201, indicating a non-significant increase. Hispanics showed negligible change over time with a slope of -0.095, an R-squared value of 0.009, and a p-value of 0.879, suggesting no significant trend. Conclusions This study examines GIST incidences across racial groups, revealing significant disparities. Whites show an increasing trend (p = 0.002), while Asians display a decreasing trend (p = 0.153), with stable rates in African Americans and Hispanics. Such disparities suggest a complex interplay of genetics, environment, and socioeconomic factors, highlighting the need for targeted research and interventions that address these differences and the systemic inequalities influencing GIST outcomes.
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Introduction Colorectal cancer (CRC) remains a significant public health challenge globally, with its pathogenesis involving the transformation of benign adenomas into malignant carcinomas. Despite advancements in screening and early detection significantly improving outcomes, the rise of digital platforms like YouTube for disseminating health information presents new challenges. Concerns over the accuracy and reliability of content underline the necessity for rigorous evaluation of these digital health education tools. Methods Our study was conducted at Nassau University Medical Center, East Meadow, New York. We meticulously analyzed YouTube videos on "colon cancer screening awareness," employing strict selection criteria to ensure both relevance and quality, focusing on English-language content with pertinent audio. Videos were evaluated for their quantitative and qualitative attributes-views, subscriber counts, likes/dislikes, comments, and content type, classifying them as scholarly or personal. We assessed video credibility through scientific accuracy using the DISCERN instrument, Global Quality Score (GQS), and Patient Education Materials Assessment Tool (PEMAT), ensuring consistency in quality and reliability evaluation among seven researchers via the intraclass correlation coefficient. These tools - DISCERN for assessing reliability and quality, GQS for evaluating overall quality, and PEMAT for understandability and actionability - facilitated a comprehensive evaluation framework. Our analysis, leveraging descriptive and inferential statistics, scrutinized differences in content quality between academic and private institutions, employing t-tests to identify statistically significant disparities. The study utilized Microsoft Excel (version 16.73, Microsoft Corporation, Redmond, Washington, United States) and IBM SPSS Statistics for Windows, version 29.0 (released 2022; IBM Corp., Armonk, New York, United States). for robust data processing and analysis, confirming the educational value and trustworthiness of the examined YouTube content. Results Our study of 156 YouTube videos on educational content, split between academic (68 videos) and private sources (88 videos), revealed significant quality differences. Using the DISCERN, PEMAT, and GQS metrics, academic videos consistently outperformed private ones, with significant margins: DISCERN (54.61 vs. 34.76), PEMAT (3.02 vs. 2.11), and GQS (3.90 vs. 2.02), supported by low p-values indicating a statistically significant superiority. These findings suggest that the source of content-academic versus private-plays a crucial role in determining the quality and reliability of educational materials on platforms like YouTube, highlighting the academic sector's commitment to higher educational standards. Conclusion The study emphasizes the critical role of credible sources in enhancing the quality of health education content on YouTube, particularly concerning CRC screening. The superiority of academic institutions in providing high-quality content suggests a need for viewers to critically assess the source of information. It also calls for enhanced regulatory oversight and measures to ensure the accuracy and reliability of health information online.
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Introduction Artificial intelligence (AI) integration in healthcare, specifically in gastroenterology, has opened new avenues for enhanced patient care and medical decision-making. This study aims to assess the reliability and accuracy of two prominent AI tools, ChatGPT 4.0 and Google Bard, in answering gastroenterology-related queries, thereby evaluating their potential utility in medical settings. Methods The study employed a structured approach where typical gastroenterology questions were input into ChatGPT 4.0 and Google Bard. Independent reviewers evaluated responses using a Likert scale and cross-referenced them with guidelines from authoritative gastroenterology bodies. Statistical analysis, including the Mann-Whitney U test, was conducted to assess the significance of differences in ratings. Results ChatGPT 4.0 demonstrated higher reliability and accuracy in its responses than Google Bard, as indicated by higher mean ratings and statistically significant p-values in hypothesis testing. However, limitations in the data structure, such as the inability to conduct detailed correlation analysis, were noted. Conclusion The study concludes that ChatGPT 4.0 outperforms Google Bard in providing reliable and accurate responses to gastroenterology-related queries. This finding underscores the potential of AI tools like ChatGPT in enhancing healthcare delivery. However, the study also highlights the need for a broader and more diverse assessment of AI capabilities in healthcare to leverage their potential in clinical practice fully.