The population attributable fraction (PAF) indicates the proportion of disease cases in a given population that can be attributed to a specific factor, assuming a causal relationship. In other words, it quantifies the extent to which that factor contributes to disease occurrence. PAF calculation methods have been applied to studies factors and several, studies have reported PAFs for dietary contributors to overall or specific cancer risks. Our team has conducted several PAF studies and presents findings on the contributions of dietary factors to cancer risk in the Korean population.Current Concepts: For colorectal cancer, the PAF of dietary factors is 34.9%, with insufficient whole grain intake contributing the largest share (16.6%). For gastric cancer, dietary factors have a PAF of 18.6%, with excessive intake of salted vegetables contributing the most (16.0%). Notably, the PAFs for inadequate whole grain and milk consumption were higher than those reported in previous studies, while the PAFs for other dietary factors fell within the expected range. These findings emphasize the need to prioritize interventions that effectively reduce the diet-attributable cancer burden.Discussion and Conclusion: Country-specific PAF estimates are crucial for developing effective cancer prevention strategies tailored to the Korean population. To better apply dietary PAF estimates, future studies should (1) integrate data from existing and ongoing cohort studies to determine Korea-specific relative risks, (2) estimate dietary prevalence using assessment tools that capture long-term dietary habits, and (3) establish optimal intake levels specific to the Korean context.

The population attributable fraction (PAF) indicates the proportion of disease cases in a given population that can be attributed to a specific factor, assuming a causal relationship. In other words, it quantifies the extent to which that factor contributes to disease occurrence. PAF calculation methods have been applied to studies factors and several, studies have reported PAFs for dietary contributors to overall or specific cancer risks. Our team has conducted several PAF studies and presents findings on the contributions of dietary factors to cancer risk in the Korean population.Current Concepts: For colorectal cancer, the PAF of dietary factors is 34.9%, with insufficient whole grain intake contributing the largest share (16.6%). For gastric cancer, dietary factors have a PAF of 18.6%, with excessive intake of salted vegetables contributing the most (16.0%). Notably, the PAFs for inadequate whole grain and milk consumption were higher than those reported in previous studies, while the PAFs for other dietary factors fell within the expected range. These findings emphasize the need to prioritize interventions that effectively reduce the diet-attributable cancer burden.Discussion and Conclusion: Country-specific PAF estimates are crucial for developing effective cancer prevention strategies tailored to the Korean population. To better apply dietary PAF estimates, future studies should (1) integrate data from existing and ongoing cohort studies to determine Korea-specific relative risks, (2) estimate dietary prevalence using assessment tools that capture long-term dietary habits, and (3) establish optimal intake levels specific to the Korean context.

The population attributable fraction (PAF) indicates the proportion of disease cases in a given population that can be attributed to a specific factor, assuming a causal relationship. In other words, it quantifies the extent to which that factor contributes to disease occurrence. PAF calculation methods have been applied to studies factors and several, studies have reported PAFs for dietary contributors to overall or specific cancer risks. Our team has conducted several PAF studies and presents findings on the contributions of dietary factors to cancer risk in the Korean population.Current Concepts: For colorectal cancer, the PAF of dietary factors is 34.9%, with insufficient whole grain intake contributing the largest share (16.6%). For gastric cancer, dietary factors have a PAF of 18.6%, with excessive intake of salted vegetables contributing the most (16.0%). Notably, the PAFs for inadequate whole grain and milk consumption were higher than those reported in previous studies, while the PAFs for other dietary factors fell within the expected range. These findings emphasize the need to prioritize interventions that effectively reduce the diet-attributable cancer burden.Discussion and Conclusion: Country-specific PAF estimates are crucial for developing effective cancer prevention strategies tailored to the Korean population. To better apply dietary PAF estimates, future studies should (1) integrate data from existing and ongoing cohort studies to determine Korea-specific relative risks, (2) estimate dietary prevalence using assessment tools that capture long-term dietary habits, and (3) establish optimal intake levels specific to the Korean context.

Conjugate upward and downward gaze palsy related to unilateral midbrain infarction is a rare neurological symptom, as there were few reported cases worldwide. Here, we report a case of 55-year-old male patient presenting with conjugate vertical gaze palsy. In this case, diffusion-weighted magnetic resonance images demonstrated a localized infarction in the right rostral midbrain.

A pulmonary artery periadventitial hematoma is a rare complication of a Stanford type A intramural hematoma. As the proximal ascending aorta and pulmonary artery share a common adventitial layer, extravasated blood from the intramural hematoma in the ascending thoracic aorta may extend to beneath the adventitia of the pulmonary artery. The authors describe a case involving a 66-year-old male with acute chest pain who presented with a pulmonary artery periadventitial hematoma associated with a Stanford type A intramural hematoma.

Purpose: This study aimed to develop a fall prediction model for community-dwelling older adults using machine learning. Methods: The present study was conducted with a secondary data analysis that used data from the 2020 national survey of older Koreans. Among 10,097 participants, data 177 were excluded due to incompleteness and 9,920 were included in the final analysis. Because of data imbalance, upsampling was performed to increase the number of individuals who fell. Forty-five independent variables for fall prediction were selected based on the fall risk factors from previous studies and univariate statistical analysis. The data were split into training and testing sets at an 80:20 ratio. Three machine learning algorithms—logistic regression, random forest, and artificial neural network—were used to develop a fall prediction model. Results: The random forest model outperformed the others, with an area under the curve of .91, accuracy of .94, precision of .94, recall of .74, and F1 score of .83. An analysis of feature importance revealed that satisfaction with health condition, visual difficulty, instrumental activities of daily living, performance of 400m walk, and cognitive ability were the top five features for fall prediction. Conclusion The fall prediction model developed using machine learning demonstrated high model performance, implying its suitability for use as a primary screening tool for fall risk. Subjective satisfaction with one’s health should be considered as an important factor in predicting falls in community-dwelling older adults. It is necessary for community health nurses to reinforce positive health awareness by continuous disease management and physical function improvement for older adults to prevent falls.

Purpose: This study aimed to develop a fall prediction model for community-dwelling older adults using machine learning. Methods: The present study was conducted with a secondary data analysis that used data from the 2020 national survey of older Koreans. Among 10,097 participants, data 177 were excluded due to incompleteness and 9,920 were included in the final analysis. Because of data imbalance, upsampling was performed to increase the number of individuals who fell. Forty-five independent variables for fall prediction were selected based on the fall risk factors from previous studies and univariate statistical analysis. The data were split into training and testing sets at an 80:20 ratio. Three machine learning algorithms—logistic regression, random forest, and artificial neural network—were used to develop a fall prediction model. Results: The random forest model outperformed the others, with an area under the curve of .91, accuracy of .94, precision of .94, recall of .74, and F1 score of .83. An analysis of feature importance revealed that satisfaction with health condition, visual difficulty, instrumental activities of daily living, performance of 400m walk, and cognitive ability were the top five features for fall prediction. Conclusion The fall prediction model developed using machine learning demonstrated high model performance, implying its suitability for use as a primary screening tool for fall risk. Subjective satisfaction with one’s health should be considered as an important factor in predicting falls in community-dwelling older adults. It is necessary for community health nurses to reinforce positive health awareness by continuous disease management and physical function improvement for older adults to prevent falls.

The manifestation of behavioral and psychological symptoms of dementia (BPSD) poses a considerable care burden and precipitates adverse health outcomes. Despite the increasing development of digital interventions, their application in the dementia population, specifically regarding their effectiveness in addressing BPSD, remains limited. Therefore, in this study, we aimed to describe a study protocol for evaluating the effectiveness of a mobile app-based individualized non-pharmacological intervention to improve BPSD in community-dwelling older adults. Methods: Employing a randomized control group pretest-posttest design, 36 dyads comprising people living with dementia (PLWD) and their family caregivers will be assigned to either an experimental or control group. The experimental group will engage in a 4-week regimen using a mobile app-based individualized non-pharmacological intervention, which includes recording and predicting BPSD. The control group will use the BPSD record system without accessing the individualized interventions. Both groups will continue with their usual care practices throughout the study period. Subsequently, an evaluation of the effectiveness of the mobile app-based individualized non-pharmacological intervention on BPSD will be conducted, which will serve as the primary outcome. Discussion: We hypothesize that the implementation of the mobile app-based individualized non-pharmacological intervention will alleviate BPSD. However, the research team may encounter several challenges owing to the novelty of digitalized interventions. Nevertheless, the results of this study will provide robust evidence regarding the efficacy of mobile app-based individualized non-pharmacological interventions for community-dwelling older PLWD.Trial registration: This trial has been registered with the Clinical Research Information Service in South Korea (CRIS No. KCT0008713; registered August 18, 2023).

Purpose: This study aimed to develop a fall prediction model for community-dwelling older adults using machine learning. Methods: The present study was conducted with a secondary data analysis that used data from the 2020 national survey of older Koreans. Among 10,097 participants, data 177 were excluded due to incompleteness and 9,920 were included in the final analysis. Because of data imbalance, upsampling was performed to increase the number of individuals who fell. Forty-five independent variables for fall prediction were selected based on the fall risk factors from previous studies and univariate statistical analysis. The data were split into training and testing sets at an 80:20 ratio. Three machine learning algorithms—logistic regression, random forest, and artificial neural network—were used to develop a fall prediction model. Results: The random forest model outperformed the others, with an area under the curve of .91, accuracy of .94, precision of .94, recall of .74, and F1 score of .83. An analysis of feature importance revealed that satisfaction with health condition, visual difficulty, instrumental activities of daily living, performance of 400m walk, and cognitive ability were the top five features for fall prediction. Conclusion The fall prediction model developed using machine learning demonstrated high model performance, implying its suitability for use as a primary screening tool for fall risk. Subjective satisfaction with one’s health should be considered as an important factor in predicting falls in community-dwelling older adults. It is necessary for community health nurses to reinforce positive health awareness by continuous disease management and physical function improvement for older adults to prevent falls.

Conjugate upward and downward gaze palsy related to unilateral midbrain infarction is a rare neurological symptom, as there were few reported cases worldwide. Here, we report a case of 55-year-old male patient presenting with conjugate vertical gaze palsy. In this case, diffusion-weighted magnetic resonance images demonstrated a localized infarction in the right rostral midbrain.