Yoxas/statistical_literacy huggingface dataset:
Diabetes mellitus is one of the growing medical problems that affect people of all ages worldwide. Education is an important part of treatment in this chronic condition. The primary objectives of diabetes education include improving knowledge and skills, changing the patient's behavior, motivating them to follow therapeutic recommendations, establishing self-care habits, and increasing their psychological resilience. The authors aimed to examine the effect of a training program on type 2 diabetic patients' self care and investigate their perspectives on the educational program. The current study used a quasi-experimental, pretest-posttest design that was conducted in Iraq. Sixty patients who met the selection criteria were included in the study. Data were collected by demographic questionnaire and the Diabetes Self-Care Scale (DSCS). Data analysis was done by independent T-tests and Paired t-tests were used to compare the scores before and after the intervention. The sample included 60 diabetic patients with more than half of them being female (55%). Most respondents aged between 50 and 60 years old, and next to half of them had only primary school education. We found that training programs can improve self-care behavior among diabetic patients so that following the intervention, the self-care score increased from 1.79 ± 0.360 to 3.17 ± 0.546 (P = 0.01). Since diabetes is a chronic condition that affects the whole individual's life, self-care plays an important role in preventing potential complications and improving quality of life. Training programs, on the other hand, increase the awareness and knowledge of patients and enable them to handle this chronic condition properly.
Diabetes numeracy skills are required in the interpretation of food labels, insulin pump dosage, the interpretation of blood glucose meter data, and the determination of carbohydrate intake. This study assessed the levels and correlates of numeracy skills in Lebanese adults with diabetes to identify those most at risk of uncontrolled diabetes. In total, 299 adults with diabetes, mean age 47.4 ± 19.8 years, took the questionnaire. It consisted of self-developed items on sociodemographic and health-related factors, in addition to the Diabetes Numeracy Test-15 (DNT-15) and the Single Item Literacy Screener. Many participants (62%) scored < 10 on the DNT-15 indicating insufficient numeracy skills. DNT-15 scores were positively associated with literacy, exercise, healthy diet, perceived diabetes control, frequency of glycaemia measurement, ability to afford treatment, and ease of understanding information related to diabetes. Age, BMI, and complications were negatively correlated with DNT-15 score. Numeracy skills were higher in males, single individuals, and in people with type 1 diabetes, fewer complications, controlled HbA1c, higher income, higher education, a prior visit to a dietician, and ability to maintain personal care despite COVID-19. Interventions to strengthen numeracy skills would empower individuals with diabetes, lead to appropriate self-management behaviors, and prevent health complications in at-risk individuals.
Diabetes self-care is explained as an illness-related coping behavior. Health literacy is an important predictor in self-care behaviors. However, little is known about their association with illness perception in Korean immigrants with diabetes. This study aimed to examine the relationships among health literacy, illness perception, and diabetes self-care in Korean-speaking immigrants with diabetes. This cross-sectional study was conducted in 2020. A convenience sample of 52 Korean adults with diabetes in Dallas-Fort Worth areas completed a survey. Data were analyzed using SPSS (version 25) with statistical significance at α < .05. The mean of health literacy was high (11.1, SD = 1.1, ranges 0-12). There were no statistically significant associations between health literacy, illness perception, and diabetes self-care. Language barriers are considered a literacy issue, but health literacy and limited English proficiency must be approached differently by health care providers.
Diabetes, a key chronic non-communicable disease, poses a substantial public health burden. The role of oral health as a determinant in the epidemiology of diabetes mellitus, particularly in the Central Eastern European region, remains underexplored. This research aims to examine the impact of specific oral health parameters, including gum bleeding, active dental caries, tooth mobility, and tooth loss, on diabetes prevalence. Additionally, it seeks to clarify the moderating effects of socio-demographic and lifestyle variables on this relationship. Data were extracted from the 2014 and 2019 datasets of the Hungarian European Health Interview Survey, comprising a combined nationally representative sample of 11,429 participants. Descriptive statistics were presented as weighted proportions and unweighted counts, and weighted Pearson's chi-squared tests were employed for assessing associations and goodness-of-fit. Significant predictors were integrated into weighted multiple logistic regression models for analysis. Sensitivity analysis was then conducted to confirm the robustness of the findings. The study identified 'Bad' self-perceived oral health as a diabetes risk (OR=1.35; 95% CI: [1.04-1.75]), with filled teeth being protective (0.65 [0.51-0.84]). Subgroup analysis revealed higher diabetes odds among individuals with primary education (1.41 [1.02-1.96]) and rural residents with tooth loss from decay (3.54 [1.36-9.19]). The bootstrap analysis with 1,000 iterations reaffirmed the model's stability and predictive accuracy for diabetes. Enhanced oral health is associated with lower risk factors for diabetes. This research highlights the importance of including oral health measures in comprehensive diabetes management approaches.
Diabetic foot ulcer is a significant complication of diabetes that carries a high mortality risk and is associated with substantial financial costs. Choosing a suitable educational model for nursing students as future nurses is necessary to improve knowledge and manage issues related to caring for diabetic patients. To determine the effect of education based on the 7E learning cycle model on the knowledge of diabetic foot ulcer prevention, critical thinking, and self-efficacy in nursing students. A single-blind, randomized controlled trial with a parallel group. This study was conducted in a nursing school and hospitals covered by it in Iran. 98 students studying in the fourth semester of the Bachelor of Nursing course. Students were divided into two distinct groups at random: an experimental group (class A, with 51 participants), and a control group (class B, with 47 participants). The participants of the experimental group were provided with an education based on the 7E learning cycle model, while the control group received conventional teaching method. Data collection methods included pre- and post-tests using research instruments (demographic questionnaire, Kaya Diabetic Foot Prevention Knowledge Questionnaire, Ricketts' Critical Thinking Disposition Questionnaire, and General Self-Efficacy Scale). The data were analyzed using descriptive statistics, non-parametric Mann-Whitney and Wilcoxon tests, parametric independent sample t-tests, ANCOVA, and paired sample t-tests. The difference in mean scores of diabetic foot ulcer prevention knowledge (Z = -6.232, p < 0.001), critical thinking (t (50) = -4.382, p < 0.001), and self-efficacy (Z = -4.427, p < 0.001) in the experimental group were statistically significant between the time interval before the intervention and one month after its completion. Compared to conventional method of teaching, education based on the 7E learning cycle model is a more effective method to improve nursing students' knowledge of diabetic foot ulcer prevention, critical thinking, and self-efficacy.
Diabetic macular edema (DME) is a significant complication of diabetes that impacts the eye and is a primary contributor to vision loss in individuals with diabetes. Early control of the related risk factors is crucial to reduce the incidence of DME. Artificial intelligence (AI) clinical decision-making tools can construct disease prediction models to aid in the clinical screening of the high-risk population for early disease intervention. However, conventional machine learning and data mining techniques have limitations in predicting diseases when dealing with missing feature values. To solve this problem, a knowledge graph displays the connection relationships of multi-source and multi-domain data in the form of a semantic network to enable cross-domain modeling and queries. This approach can facilitate the personalized prediction of diseases using any number of known feature data. In this study, we proposed an improved correlation enhancement algorithm based on knowledge graph reasoning to comprehensively evaluate the factors that influence DME to achieve disease prediction. We constructed a knowledge graph based on Neo4j by preprocessing the collected clinical data and analyzing the statistical rules. Based on reasoning using the statistical rules of the knowledge graph, we used the correlation enhancement coefficient and generalized closeness degree method to enhance the model. Meanwhile, we analyzed and verified these models' results using link prediction evaluation indicators. The disease prediction model proposed in this study achieved a precision rate of 86.21%, which is more accurate and efficient in predicting DME. Furthermore, the clinical decision support system developed using this model can facilitate personalized disease risk prediction, making it convenient for the clinical screening of a high-risk population and early disease intervention.
Diabetic patients' oral health concerns are a reality for every diabetic patient in China. The attitudes of diabetic patients toward early identification of oral literacy influence oral literacy in diabetes patients. Diabetes patients' oral health literacy is critical for providing focused education and therapies. However, no instrument exists to measure oral health literacy in Chinese diabetic patients. In this study, the English version of the oral health literacy among people living with diabetes (OHLD) scale was cross-culturally validated to provide a reliable tool for assessing the oral health literacy of diabetic patients in China. The oral literacy among people living with diabetes (OHLD) scale was Chineseized and its reliability and validity tested, and the OHLD scale was revised to test the reliability and validity of the Chinese version of the OHLD scale and to provide a tool for assessing the oral health literacy of diabetic patients in China. A modified version of the Brislin translation model was used, cross-cultural adaptation was performed through expert consultation and pre-survey, and expert opinion was used to assess content validity to form the Chinese version of the Oral Health Literacy Scale for Diabetic Patients, which was administered to 420 diabetic patients in two tertiary hospitals in Liaoning Province from March to August 2023. The reliability of the scale was tested. SPSS 25.0 and AMOS 23.0 were used to analyze the data. The Chinese version of the OHLD scale consisted of three dimensions and 10 entries. Structure of the validity analysis: three factors were extracted from the exploratory factors with a cumulative variance contribution of 79.794%; Content validity results:An item's content validity index (I-CVI) was 0.857 to 1 at the entry level, and the content validity index of the scale (S-CVI) was 0.928 at the scale level. The results of the reliability analyses were: the Cronbach's alpha coefficient for the total scale was 0.908; the Cronbach's alpha coefficients for the factors and dimensions were 0.853 to 0.922; the split-half reliability was 0.827; and the test-retest reliability was 0.848. The results of the validation factor analysis showed that (χ2/df) was 1.430, the root mean square of the error of approximation (RMSEA) was 0.045, the comparative fit index (CFI) was 0.989, and the Tucker Lewis index (TLI) was 0.985, which showed that the model had an overall good fit. The Chinese version of the OHLD scale has good reliability and validity and can be used as a valid tool for assessing diabetes mellitus patients in China.
Diabetic retinopathy (DR) is the most common diabetic complication, which usually leads to retinal damage, vision loss, and even blindness. A computer-aided DR grading system has a significant impact on helping ophthalmologists with rapid screening and diagnosis. Recent advances in fundus photography have precipitated the development of novel retinal imaging cameras and their subsequent implementation in clinical practice. However, most deep learning-based algorithms for DR grading demonstrate limited generalization across domains. This inferior performance stems from variance in imaging protocols and devices inducing domain shifts. We posit that declining model performance between domains arises from learning spurious correlations in the data. Incorporating do-operations from causality analysis into model architectures may mitigate this issue and improve generalizability. Specifically, a novel universal structural causal model (SCM) was proposed to analyze spurious correlations in fundus imaging. Building on this, a causality-inspired diabetic retinopathy grading framework named CauDR was developed to eliminate spurious correlations and achieve more generalizable DR diagnostics. Furthermore, existing datasets were reorganized into 4DR benchmark for DG scenario. Results demonstrate the effectiveness and the state-of-the-art (SOTA) performance of CauDR. Diabetic retinopathy (DR) is the most common diabetic complication, which usually leads to retinal damage, vision loss, and even blindness. A computer-aided DR grading system has a significant impact on helping ophthalmologists with rapid screening and diagnosis. Recent advances in fundus photography have precipitated the development of novel retinal imaging cameras and their subsequent implementation in clinical practice. However, most deep learning-based algorithms for DR grading demonstrate limited generalization across domains. This inferior performance stems from variance in imaging protocols and devices inducing domain shifts. We posit that declining model performance between domains arises from learning spurious correlations in the data. Incorporating do-operations from causality analysis into model architectures may mitigate this issue and improve generalizability. Specifically, a novel universal structural causal model (SCM) was proposed to analyze spurious correlations in fundus imaging. Building on this, a causality-inspired diabetic retinopathy grading framework named CauDR was developed to eliminate spurious correlations and achieve more generalizable DR diagnostics. Furthermore, existing datasets were reorganized into 4DR benchmark for DG scenario. Results demonstrate the effectiveness and the state-of-the-art (SOTA) performance of CauDR.
Diabetic retinopathy is a leading cause of vision impairment globally. Vision loss from diabetic retinopathy can generally be prevented by early detection and timely treatment. The WHO included a measure of service access for diabetic retinopathy as a core indicator in the Eye Care Indicator Menu launched in 2022: retina screening coverage for people with diabetes. The aim of this review is to provide a comprehensive global and regional summary of the available information on retina screening coverage for people with diabetes. A search will be conducted in five databases without language restrictions for studies from any country reporting retina screening coverage for adults with any type of diabetes at the national or subnational level using data collected since 1 January 2000 until the search date. We will also seek reports and coverage statistics from government websites of all WHO member states. Two investigators will independently screen studies, extract relevant data and assess risk of bias of included studies. The results of the review will be reported using the Preferred Reporting Items for Systematic Review and Meta-Analysis guideline. We will summarise the range of coverage definitions reported across included studies and present the median retina screening coverage in WHO regions and by World Bank country income level. Depending on the availability of data, we will conduct meta-analysis to assess disparities in retina screening coverage for people with diabetes by factors in the PROGRESS framework (Place of residence, Race/ethnicity/culture/language, Occupation, Gender/sex, Religion, Education, Socioeconomic status and Social capital). This review will only include published data thus no ethical approval will be sought. The findings of this review will be published in a peer-reviewed journal and presented at relevant conferences. OSF registration 17/10/2023: https://osf.io/k5p69.
Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition defines gender identity disorder (GID) as a strong and persistent identification with the opposite sex and the distress that may accompany the incongruence between one's experienced or expressed gender and one's assigned gender. The onset of GID commonly begins early in childhood. Gender dysphoria has a higher prevalence of other comorbid psychiatric illnesses, such as mood, anxiety, and adjustment disorders, with increased suicide incidence and self-harming behaviors than the general population. Studies show that some temperamental, environmental, genetic, and psychological factors play a role in developing GID. Approximately 16% of transgender people and 21% of transgender women get incarcerated compared with the general US population. During incarceration, they face many issues, such as victimization, severe verbal harassment, purposeful humiliation, unwanted sexual advances, physical assault, forcible sex, and unwanted strip searches. There is a need for a better understanding of the issues and needs of this population to promote positive outcomes.
Diagnostic autopsy is the most reliable approach to definitively ascertain the cause of death and evaluate the accuracy of antemortem clinical diagnoses. Identifying diagnostic discrepancies is vital to understanding common gaps in antemortem clinical diagnoses and modifying antemortem diagnostic approaches to increase the accuracy of clinical diagnosis. The objective of this study was to determine the frequency of diagnostic discrepancies between antemortem clinical diagnoses and postmortem autopsies on lung pathologies and to understand the reasons for diagnostic discrepancies among cases included in Child Health and Mortality Prevention Surveillance (CHAMPS) in Ethiopia. A clinical case series study of deaths among children under-five in the CHAMPS study at three sites in Ethiopia between October 2019 and April 2022 was conducted. The antemortem clinical diagnoses and postmortem pathological diagnoses of the lung were compared for each case. Two senior physicians assessed the findings for both agreement and disagreement. McNemar's test was used to assess for statistically significant differences between antemortem and postmortem diagnoses. Seventy-five cases were included (73.3% male). Over half (54.7%) died between the 1st and 7th day of life. Sepsis (66.7%), pneumonia (6.7%), and meconium aspiration syndrome (5.0%) were the most common immediate causes of death. Half (52%) of cases were correctly diagnosed antemortem. The magnitude of diagnostic discrepancy was 35% (95% CI: 20-47%). The most common contributing factors to diagnostic discrepancy were gaps in knowledge (22/75, 35.5%) and problems in consultation and teamwork (22/75, 35.5%). Misdiagnoses were common among young children who died with positive lung pathology findings. In-service education initiatives and multidisciplinary collaboration are needed to mitigate high rates of diagnostic discrepancies among young children to potentially prevent future deaths.
Diagnostic errors pose a significant risk to patient safety and have substantial medical and economic consequences. Despite their importance, diagnostic error education is currently lacking in standard pre-graduate curricula. This study aimed to investigate the incidence of diagnostic errors and the frequency of recognition among medical students in Japan. A pilot survey was conducted immediately after the General Medicine In-Training Examination (GM-ITE), a comprehensive post-graduation test, administered to new residents right after graduation from medical school. The survey assessed whether they received education on diagnostic errors during their formal undergraduate medical education and whether they recognized diagnostic errors during their clinical training. Of the 564 examinees, 421 participated in the study. The majority of participants (63.9 %) reported receiving education on diagnostic errors, and 15.7 % recognized diagnostic errors during their clinical training. Significantly, those who received education on diagnostic errors had a higher rate of recognizing such errors compared to those who did not (19.7 vs. 8.6 %; p=0.0017). These findings suggest that the recognition rate of diagnostic errors increases with improved literacy in diagnostic error education. This highlights the importance of incorporating diagnostic error education into medical curricula to develop effective strategies to prevent and manage diagnostic errors, and thereby enhance medical and patient safety. However, this study did not examine the specific educational content of the errors or the details of the recognition, necessitating further investigation in the future.
Diagnostic tests play a crucial role in establishing the presence of a specific disease in an individual. Receiver Operating Characteristic (ROC) curve analyses are essential tools that provide performance metrics for diagnostic tests. Accurate determination of the cutoff point in ROC curve analyses is the most critical aspect of the process. A variety of methods have been developed to find the optimal cutoffs. Although the R programming language provides a variety of package programs for conducting ROC curve analysis and determining the appropriate cutoffs, it typically needs coding skills and a substantial investment of time. Specifically, the necessity for data preprocessing and analysis can present a significant challenge, especially for individuals without coding experience. We have developed the CERA (ChatGPT-Enhanced ROC Analysis) tool, a user-friendly ROC curve analysis web tool using the shiny interface for faster and more effective analyses to solve this problem. CERA is not only user-friendly, but it also interacts with ChatGPT, which interprets the outputs. This allows for an interpreted report generated by R-Markdown to be presented to the user, enhancing the accessibility and understanding of the analysis results.
Dialysis has been the dominant treatment regimen in end-stage kidney disease as a means to remove uremic waste products and to maintain electrolyte, acid base, and fluid balance. However, given that dialysis may not always provide a survival benefit nor improved quality of life in certain subpopulations, there is growing recognition of the need for conservative and preservative management as an alternative treatment strategy for advanced chronic kidney disease (CKD). Personalized nutritional management tailored to patient's sociodemographics, social needs, psychological status, health literacy level, and preferences is a key component of conservative and preservative care, as well as in the management of patients transitioning from non-dialysis dependent CKD to dialysis. In this review, we discuss the nutritional and metabolic alterations that ensue in CKD; the rationale for low-protein diets in the conservative and preservative management of advanced CKD; the role of plant-based diets in kidney health; emerging data on dietary potassium and sodium intake on CKD outcomes; and the practical implementation of dietary interventions in advanced kidney disease.