Prevalence and Risk Factors of Constipation Symptoms among Patients Undergoing Colonoscopy: A Single-Center Cross-Sectional Study.

INTRODUCTION: Constipation is one of the most common gastrointestinal symptoms. It may compromise quality of life and social functioning and result in increased healthcare use and costs. We aimed to evaluate the prevalence and risk factors of constipation symptoms, as well as those of refractory constipation symptoms among patients who underwent colonoscopy. METHODS: Over 4.5 years, patients who underwent colonoscopy and completed questionnaires were analyzed. Patients' symptoms were evaluated using the Gastrointestinal Symptoms Rating Scale. RESULTS: Among 8,621 eligible patients, the prevalence of constipation symptoms was 33.3%. Multivariate analysis revealed female sex (odds ratio [OR] 1.7, p < 0.001), older age (OR 1.3, p < 0.001), cerebral stroke with paralysis (OR 1.7, p = 0.009), chronic renal failure (OR 2.6, p < 0.001), ischemic heart disease (OR 1.3, p = 0.008), diabetes (OR 1.4, p < 0.001), chronic obstructive pulmonary disease (OR 1.5, p = 0.002), benzodiazepine use (OR 1.7, p < 0.001), antiparkinsonian medications use (OR 1.9, p = 0.030), and opioid use (OR 2.1, p = 0.002) as independent risk factors for constipation symptoms. The number of patients taking any medication for constipation was 1,134 (13.2%); however, refractory symptoms of constipation were still present in 61.4% of these patients. Diabetes (OR 1.5, p = 0.028) and irritable bowel syndrome (OR 3.1, p < 0.001) were identified as predictors for refractory constipation symptoms. CONCLUSIONS: Constipation occurred in one-third of patients, and more than half of patients still exhibited refractory symptoms of constipation despite taking laxatives. Multiple medications and concurrent diseases seem to be associated with constipation symptoms.

Evaluating the understandability and actionability of online CKD educational materials.

BACKGROUND: Previous studies have not fully determined whether online education materials on chronic kidney disease (CKD) for Japanese patients are easy to understand and help change their behavior. Therefore, this study quantitatively assessed the understandability and actionability of online CKD education materials. METHODS: In September 2021, we searched Google and Yahoo Japan using the keywords "kidney," "kidney disease," "CKD," "chronic kidney disease," and "renal failure" to identify 538 webpages. We used the Japanese version of the Patient Education Materials Assessment Tool (PEMAT), ranging from 0 to 100%, to evaluate the understandability and actionability of webpages. We set the cutoff point to 70%. RESULTS: Of the 186 materials included, the overall understandability and actionability were 61.5% (± 16.3%) and 38.7% (± 30.6%), respectively. The materials were highly technical in their terminology and lacked clear and concise charts and illustrations to encourage action. Compared to lifestyle modification materials on CKD overview, symptoms/signs, examination, and treatment scored significantly lower on the PEMAT. In addition, the materials produced by medical institutions and academic organizations scored significantly lower than those produced by for-profit companies. CONCLUSION: Medical institutions and academic organizations are encouraged to use plain language and to attach explanations of medical terms when preparing materials for patients. They are also expected to improve visual aids to promote healthy behaviors.

Performance of ChatGPT Across Different Versions in Medical Licensing Examinations Worldwide: Systematic Review and Meta-Analysis.

BACKGROUND: Over the past 2 years, researchers have used various medical licensing examinations to test whether ChatGPT (OpenAI) possesses accurate medical knowledge. The performance of each version of ChatGPT on the medical licensing examination in multiple environments showed remarkable differences. At this stage, there is still a lack of a comprehensive understanding of the variability in ChatGPT's performance on different medical licensing examinations. OBJECTIVE: In this study, we reviewed all studies on ChatGPT performance in medical licensing examinations up to March 2024. This review aims to contribute to the evolving discourse on artificial intelligence (AI) in medical education by providing a comprehensive analysis of the performance of ChatGPT in various environments. The insights gained from this systematic review will guide educators, policymakers, and technical experts to effectively and judiciously use AI in medical education. METHODS: We searched the literature published between January 1, 2022, and March 29, 2024, by searching query strings in Web of Science, PubMed, and Scopus. Two authors screened the literature according to the inclusion and exclusion criteria, extracted data, and independently assessed the quality of the literature concerning Quality Assessment of Diagnostic Accuracy Studies-2. We conducted both qualitative and quantitative analyses. RESULTS: A total of 45 studies on the performance of different versions of ChatGPT in medical licensing examinations were included in this study. GPT-4 achieved an overall accuracy rate of 81% (95% CI 78-84; P<.01), significantly surpassing the 58% (95% CI 53-63; P<.01) accuracy rate of GPT-3.5. GPT-4 passed the medical examinations in 26 of 29 cases, outperforming the average scores of medical students in 13 of 17 cases. Translating the examination questions into English improved GPT-3.5's performance but did not affect GPT-4. GPT-3.5 showed no difference in performance between examinations from English-speaking and non-English-speaking countries (P=.72), but GPT-4 performed better on examinations from English-speaking countries significantly (P=.02). Any type of prompt could significantly improve GPT-3.5's (P=.03) and GPT-4's (P<.01) performance. GPT-3.5 performed better on short-text questions than on long-text questions. The difficulty of the questions affected the performance of GPT-3.5 and GPT-4. In image-based multiple-choice questions (MCQs), ChatGPT's accuracy rate ranges from 13.1% to 100%. ChatGPT performed significantly worse on open-ended questions than on MCQs. CONCLUSIONS: GPT-4 demonstrates considerable potential for future use in medical education. However, due to its insufficient accuracy, inconsistent performance, and the challenges posed by differing medical policies and knowledge across countries, GPT-4 is not yet suitable for use in medical education. TRIAL REGISTRATION: PROSPERO CRD42024506687; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=506687.

Misinformation surrounding sodium reduction for blood pressure: content analysis of Japanese posts on X.

This content analysis aimed to assess misinformation themes regarding sodium reduction and blood pressure on X with the goal of providing strategies to address and debunk such misinformation. A total of 531 posts were manually coded into sodium-related misinformation themes, with inclusion criteria for posts asserting no association between sodium reduction and hypertension or claiming consuming sodium is beneficial for health. Numbers and post frequencies per misinformation theme were calculated. Post characteristics, including information sources, advertisements and narratives, were coded, and a correlation analysis was conducted to assess their association with each misinformation theme. Fourteen sodium-related misinformation themes were identified and consistently disseminated on X. The predominant theme, 'Natural Salt', accounted for 37.7% (n = 200), reaching 1.6 million followers, followed by 'Reducing salt could be bad for my health' theme, comprising 28.6% (n = 152) and reaching 1.5 million followers. There was a statistical correlation between the natural salt misinformation theme and advertisements. Many of the most frequent misinformation themes identified in this study have not been systematically debunked by organizations such as the World Health Organization and the American Heart Association. This study underscores the importance of continuous monitoring and analysis of sodium-related misinformation on social media platforms and their underlying commercial interests. Such monitoring has the potential to identify prevalent misinformation themes that may pose harm to the public and to inform public health organizations, enabling them to proactively address potential issues through debunking.