Sarcopenia is a common problem among the elderly worldwide. Muscle mass can decrease with aging and decreased physical activity may occur. However, the sarcopenia prevalence among community dwelling older adults living in snow-covered cities remains largely unknown. Therefore, we evaluated the prevalence of and risk factors for sarcopenia in this population aged 65 years or older according to the definitions and diagnoses of the European Working Group on Sarcopenia in Older People-2 from two welfare centers in Sapporo, Hokkaido, Japan. The demographic characteristics, nutrition, and depression status of 310 participants were assessed using a standardized questionnaire. All participants were assessed for grip strength. Skeletal muscle index, body mass index (BMI), and total body water (TBW) were measured using bioelectrical impedance analysis. The overall sarcopenia prevalence in the study population was 8.1%. Multivariate analysis revealed that diabetes and taking more than four drugs per day were independently associated with sarcopenia (adjusted odds ratio (OR) = 3.66, 95% confidence interval (CI) = 1.06–12.61; OR = 2.66, CI = 1.05–6.77, respectively). BMI and TBW were negatively associated with sarcopenia. Sarcopenia prevalence was low in community dwelling older adults living in the snow-covered city. It is indicated that welfare center exercise may be a good intervention for the prevention of sarcopenia. Moreover, the management of diabetes, medication, and nutrition is necessary for sarcopenia prevention in community dwelling older adults.

Most studies have associated denture wearing with a higher risk of malnutrition and lower nutritional intake in the elderly. As the effect of wearing dentures remains debatable, the aim of this study was to determine if older adults wearing dentures have a higher risk of malnutrition. We recruited 294 Japanese community-dwelling adults aged ≥65 years from two elderly welfare centers in Sapporo. The Mini Nutritional Assessment-Short Form (MNA-SF) was used to assess their nutritional status. Their demographic characteristics, eating habits, and health status were assessed using a questionnaire. Moreover, salt-impregnated taste paper and bioelectrical impedance analysis were used to obtain salt taste sensitivity and body composition (body mass index and protein mass). The mean age of older adults was 75.8 ± 5.8 years and 71.1% were women. We found that 74.1% of the older adults wore dentures. The proportion of the whole population with a risk of malnutrition was 23.5%. Multivariate analysis revealed that partial denture wearing could be protective against the risk of malnutrition. In this study, we found more partial denture wearers with normal nutritional conditions and cooking by themselves than non-denture-wearing older adults. Furthermore, complete denture wearers were eating significantly less meats or fish every day. This study suggests that using partial dentures to replace the lost teeth to meet the dietary needs of the elderly is critical to maintaining a healthy nutritional status. Moreover, it also indicates the importance of providing education to older adults who wear complete dentures.

We aimed to develop and validate a nomogram for the individualized prediction of the risk of post-stroke fatigue (PSF) after discharge. Fatigue was measured using the Fatigue Assessment Scale. Multivariable logistic regression analysis was applied to build a prediction model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the predictive model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was conducted using bootstrapping validation. Finally, a web application was developed to facilitate the use of the nomogram. We developed a nomogram based on 95 stroke patients. The predictors included in the nomogram were sex, pre-stroke sarcopenia, acute phase fatigue, dysphagia, and depression. The model displayed good discrimination, with a C-index of 0.801 (95% confidence interval: 0.700-0.902) and good calibration. A high C-index value of 0.762 could still be reached in the interval validation. Decision curve analysis showed that the risk of PSF after discharge was clinically useful when the intervention was decided at the PSF risk possibility threshold of 10% to 90%. This nomogram could be conveniently used to provide an individual, visual, and precise prediction of the risk probability of PSF after being discharged home. Thus, as an aid in decision-making, physicians and other healthcare professionals can use this predictive method to provide early intervention or a discharge plan for stroke patients during the hospitalization period.

Objectives: Large language models (LLMs) are increasingly used in healthcare, with the potential for various applications. However, the performance of different LLMs on nursing license exams and their tendencies to make errors remain unclear. This study aimed to evaluate the accuracy of LLMs on basic nursing knowledge and identify trends in incorrect answers. Methods: The dataset consisted of 692 questions from the Japanese national nursing examinations over the past 3 years (2021–2023) that were structured with 240 multiple-choice questions per year and a total score of 300 points. The LLMs tested were ChatGPT-3.5, ChatGPT-4, and Microsoft Copilot. Questions were manually entered into each LLM, and their answers were collected. Accuracy rates were calculated to assess whether the LLMs could pass the exam, and deductive content analysis and Chi-squared tests were conducted to identify the tendency of incorrect answers. Results: For over 3 years, the mean total score and standard deviation (SD) using ChatGPT-3.5, ChatGPT-4, and Microsoft Copilot was 180.3 ± 22.2, 251.0 ± 13.1, and 256.7 ± 14.0, respectively. ChatGPT-4 and Microsoft Copilot showed sufficient accuracy rates to pass the examinations for all the years. All LLMs made more mistakes in the health support and social security system domains ( p < 0.01). Conclusions: ChatGPT-4 and Microsoft Copilot may perform better than Chat GPT-3.5, and LLMs could incorrectly answer questions about laws and demographic data specific to a particular country.

This qualitative study explored the decision-making process regarding continuation of systemic therapy among older adults with advanced cancer, focusing on how treatment choices are interpreted within the context of aging and remaining life. Seventeen participants aged ≥70 years with recurrent or metastatic cancer receiving systemic therapy were recruited from two cancer centers in Japan. Semi-structured interviews were conducted and analyzed using a modified grounded theory approach. The analysis identified six interrelated categories describing a dynamic decision-making process. Central to this process was discovering a moderately acceptable state, which functioned as a turning point through which participants recalibrated expectations of treatment and health in relation to aging. This turning point enabled participants to integrate cancer treatment into the aging process rather than perceiving treatment and aging as competing forces. Throughout the process, adopting more flexible views of health shaped how participants evaluated treatment burden, partial effectiveness, and acceptability over time. These findings highlight treatment continuation as an evolving interpretive process grounded in aging and uncertainty, underscoring the importance of nursing practices that support older adults in reflecting on lived experiences and making treatment decisions aligned with their values in later life.