Yoxas/statistical_literacy huggingface dataset:
The Internet is an important source of information for patients, but its effectiveness relies on the readability of its content. Patient education materials (PEMs) should be written at or below a sixth-grade reading level as outlined by agencies such as the American Medical Association. This study assessed PEMs' readability for the novel anterior vertebral body tethering (AVBT), distraction-based methods, and posterior spinal fusion (PSF) in treating pediatric spinal deformity. An online search identified PEMs using the terms "anterior vertebral body tethering," "growing rods scoliosis," and "posterior spinal fusion pediatric scoliosis." We selected the first 20 general medical websites (GMWs) and 10 academic health institution websites (AHIWs) discussing each treatment (90 websites total). Readability tests for each webpage were conducted using Readability Studio software. Reading grade levels (RGLs), which correspond to the US grade at which one is expected to comprehend the text, were calculated for sources and independent t tests compared with RGLs between treatment types. The mean RGL was 12.1 ± 2.0. No articles were below a sixth-grade reading level, with only 2.2% at the sixth-grade reading level. AVBT articles had a higher RGL than distraction-based methods (12.7 ± 1.6 vs 11.9 ± 1.9, P = 0.082) and PSF (12.7 ± 1.6 vs 11.6 ± 2.3, P = 0.032). Materials for distraction-based methods and PSF were comparable (11.9 ± 1.9 vs 11.6 ± 2.3, P = 0.566). Among GMWs, AVBT materials had a higher RGL than distraction-based methods (12.9 ± 1.4 vs 12.1 ± 1.8, P = 0.133) and PSF (12.9 ± 1.4 vs 11.4 ± 2.4, P = 0.016). Patients' health literacy is important for shared decision-making. Assessing the readability of scoliosis treatment PEMs guides physicians when sharing resources and discussing treatment with patients. Both GMWs and AHIWs exceed recommended RGLs, which may limit patient and parent understanding. Within GMWs, AVBT materials are written at a higher RGL than other treatments, which may hinder informed decision-making and patient outcomes. Efforts should be made to create online resources at the appropriate RGL. At the very least, patients and parents may be directed toward AHIWs; RGLs are more consistent.
The internet is an increasingly important resource for patients seeking health-related information. Because of this trend, the American Medical Association (AMA) and National Institutes of Health (NIH) recommend that online patient education materials (PEMs) be written between a third and seventh grade level. The present study evaluates the readability levels of ophthalmological PEMs provided by five major academic hospitals, quantifies the availability of accompanying videos and graphics, and examines the extent to which readability may be increased. In March 2021, 397 PEMs from five major academic hospitals were extracted for subsequent analysis by seven validated readability assessments. The presence of an accompanying video or graphic was noted. Statistical significance was assessed using the Kruskal-Wallis test with Dunn's multiple comparisons test and the chi-square test. Nearly all articles were written above the recommended reading level of 7th grade. After averaging the scales for each article, the median grade level was 11.7 (interquartile range [IQR], 10.7-12.7). The PEMs with the highest median reading level were provided by the Johns Hopkins University Wilmer Institute (12.6, IQR, 11.3 - 13.6). Only 13.6% and 13.1% of articles had an accompanying video and graphic, respectively. Reduction of sentence length beneath 15 words resulted in an improvement of readability by 2.7 grade levels. The readability of online patient resources provided by major academic hospitals were above the literacy guidelines recommended by the NIH and AMA. Furthermore, most articles did not include a video or graphic, both of which could potentially improve patient understandability of educational materials. By altering these PEMs, as demonstrated here, institutions could increase the value these articles provide for patients and therefore the quality of the patient-physician relationship.
The internet is increasingly used as a source of health information. This study aimed to explore the online oral health information seeking experience, to determine the knowledge, attitudes and practices (KAP) of oral health, and to investigate the associations between online oral health information seeking experience and oral health KAP of participants. A cross-sectional online survey was conducted. Three hundred and ninety-five university students participated in the study. Required data were gathered using two valid questionnaires eHIQ (e-Health Information Questionnaire) and Oral Health Knowledge, Attitude and Behavior Questionnaire. eHIQ was a 2-part instrument with 37 items. eHIQ-Part 1 includes 11 items related to general views of using the internet in relation to health. eHIQ-Part 2 includes 26 items related to the consequences of using specific health-related online sources. The second questionnaire includes 30 items as a combination of multiple-choice and yes/no type questions. The data were analyzed using the statistical analysis software SPSS version 20. Mean scores, standard deviation, and frequency distribution were obtained. Independent T-test, correlation coefficients and analysis of variance (ANOVA) were used in the analysis. Participants had good KAP of oral health. The between-group differences tests showed that oral health knowledge and attitudes were significantly different between gender and years of study groups, but the differences of oral health practices were significant only based on years of study. Participants had moderate scores regarding all sub-scales of eHIQ-Parts 1 and 2. Findings revealed that online oral health information seeking behavior was associated with oral health KAP (p < 0.05). According to the results the general views of using the internet in relation to health and the consequences of using specific health-related online sources were in a moderate level among the participants. Such results can emphasize the need for more planning, education and empowerment of the population`s health literacy. The present study also provides good insights for the latter and has practical and policy implications besides its research values.
The internet is increasingly used as a source of health-related information by individuals making a medical decision. Online consultation services offer a safe and anonymous time- and place-independent space in which users can ask health-related questions combined with related individual health narratives, including associated emotions. Research on emotions in health narratives and medical communication is still at an early stage. This study investigates the users of an online consultation service themselves and the content of their inquiries related to health narratives, queried diseases, and the emotions expressed. An automated content analysis of all online inquiries to the University Hospital Zurich from 09/08/1999 to 07/06/2018 was conducted (N = 55,476 inquiries). The majority of users were female. Over time, however, significantly more men and older adults began to submit inquiries. The wide variety of medical inquiries submitted to the service extended across all categories in the International Statistical Classification of Diseases and correlated with the statistical incidence of diseases in Switzerland. In line with theoretical assumptions about mood management and mood adjustment, users' health narratives most frequently expressed negative emotions like suffering, fear, worry, and shame. The results find support for a close link between health narratives and emotions. By providing an anonymous space where users can talk about diseases that may be a source of stigma or shame, online consultation services can empower patients and promote health literacy through the provision of individualized health information.
The internet is most people's primary source of (health) information. However, no validated instrument exists to assess eHealth literacy in the group of patient with cardiac diseases. The objective of this study was the evaluation of the psychometric properties of the German revised version of the eHealth literacy scale (GR-eHEALS) in individuals with coronary artery disease (CAD) and congestive heart failure (CHF). A cross-sectional study was conducted. N = 455 were included in the statistical analyses. The assessment compromised the GR-eHEALS, medical history, sociodemographic data, and technology-related data. Confirmatory factor analyses, correlational analyses, and tests of measurement invariance were performed. The two-factorial model reached a good model fit. The sub-scales information seeking and information appraisal, as well as the eHealth literacy total score, reached high reliability coefficients. Construct and criterion validity was fully confirmed For the two-factorial model, measurement invariance up to the scalar level could be confirmed regarding the sociodemographic characteristics sex, age, and educational level. This study confirmed the two-factor structure, construct, and criterion validity as well as measurement invariance at the scalar level for sex, age, and educational level of the GR-eHEALS scale in a sample of individuals with CAD and CHF.
The Internet of Battlefield Things (IoBT) refers to interconnected battlefield equipment/sources for synchronized automated decision making. Due to difficulties unique to the battlefield, such as a lack of infrastructure, the heterogeneity of equipment, and attacks, IoBT networks differ significantly from regular IoT networks. In war scenarios, real-time location information gathering is critical for combat effectiveness and is dependent on network connectivity and information sharing in the presence of an enemy. To maintain connectivity and guarantee the safety of soldiers/equipment, location information must be exchanged. The location, identification, and trajectory of soldiers/devices are all contained in these messages. A malicious attacker may utilize this information to build a complete trajectory of a target node and track it. This paper proposes a location privacy-preserving scheme in IoBT networks using deception-based techniques. Dummy identifier (DID), sensitive areas location privacy enhancement, and silence period concepts are used to minimize the attacker's ability to track a target node. In addition, to consider the security of the location information, another security layer is proposed, which generates a pseudonym location for the source node to use instead of its real location when sending messages in the network. We develop a Matlab simulation to evaluate our scheme in terms of average anonymity and probability of linkability of the source node. The results show that the proposed method improves the anonymity of the source node. It reduces the attacker's ability to link the old DID of the source node with its new DID. Finally, the results show further privacy enhancement by applying the sensitive area concept, which is important for IoBT networks.
The interplay of stochastic and ecological processes that govern the establishment and persistence of host-associated microbial communities is not well understood. Here we illustrate the conceptual and practical advantages of fitting stochastic population dynamics models to multi-species bacterial time series data. We show how the stability properties, fluctuation regimes and persistence probabilities of human vaginal microbial communities can be better understood by explicitly accommodating three sources of variability in ecological stochastic models of multi-species abundances: 1) stochastic biotic and abiotic forces, 2) ecological feedback and 3) sampling error. Rooting our modeling tool in stochastic population dynamics modeling theory was key to apply standardized measures of a community's reaction to environmental variation that ultimately depends on the nature and intensity of the intra-specific and inter-specific interaction strengths. Using estimates of model parameters, we developed a Risk Prediction Monitoring (RPM) tool that estimates temporal changes in persistence probabilities for any bacterial group of interest. This method mirrors approaches that are often used in conservation biology in which a measure of extinction risks is periodically updated with any change in a population or community. Additionally, we show how to use estimates of interaction strengths and persistence probabilities to formulate hypotheses regarding the molecular mechanisms and genetic composition that underpin different types of interactions. Instead of seeking a definition of "dysbiosis" we propose to translate concepts of theoretical ecology and conservation biology methods into practical approaches for the management of human-associated bacterial communities.
The interpretation of colors in visualizations is facilitated when the assignments between colors and concepts in the visualizations match human's expectations, implying that the colors can be interpreted in a semantic manner. However, manually creating a dataset of suitable associations between colors and concepts for use in visualizations is costly, as such associations would have to be collected from humans for a large variety of concepts. To address the challenge of collecting this data, we introduce a method to extract color-concept associations automatically from a set of concept images. While the state-of-the-art method extracts associations from data with supervised learning, we developed a self-supervised method based on colorization that does not require the preparation of ground truth color-concept associations. Our key insight is that a set of images of a concept should be sufficient for learning color-concept associations, since humans also learn to associate colors to concepts mainly from past visual input. Thus, we propose to use an automatic colorization method to extract statistical models of the color-concept associations that appear in concept images. Specifically, we take a colorization model pre-trained on ImageNet and fine-tune it on the set of images associated with a given concept, to predict pixel-wise probability distributions in Lab color space for the images. Then, we convert the predicted probability distributions into color ratings for a given color library and aggregate them for all the images of a concept to obtain the final color-concept associations. We evaluate our method using four different evaluation metrics and via a user study. Experiments show that, although the state-of-the-art method based on supervised learning with user-provided ratings is more effective at capturing relative associations, our self-supervised method obtains overall better results according to metrics like Earth Mover's Distance (EMD) and Entropy Difference (ED), which are closer to human perception of color distributions.
The interpretation of gravity anomalies is crucial for identifying subsurface mineralized targets and understanding the density variations between the targets and the surrounding structures. To confirm the presence of ore and mineral targets, simple geometric bodies are often used. One of the commonly used global metaheuristic algorithms for gravity data analysis is the particle optimization algorithm. In this study, we employed this method to determine the parameters of buried bodies that resemble finite vertical cylinders by inferring gravity anomalies profiles (amplitude coefficient, depth to top, depth to bottom, origin, and length of the target representing the difference between two depths). The algorithm utilizes particle movement to identify the best way to reach the global or optimum solution. The algorithm's performance was evaluated on synthetic-examples with and without noise (5 % and 10 % levels) and also verified on a real dataset for mineral exploration from Canada. The results showed that the algorithm's stability and accuracy were not affected by the presence of noise and multi-models. Moreover, the field case results were consistent with the existing geological information, borehole data, and previously published outcomes.
The interpretation of ventilator waveforms is essential for effective and safe mechanical ventilation but requires specialized training and expertise. This study aimed to investigate the ability of ICU professionals to interpret ventilator waveforms, identify areas requiring further education and training, and explore the factors influencing their interpretation skills. We conducted an international online anonymous survey of ICU professionals (physicians, nurses, and respiratory therapists [RTs]), with ≥ 1 y of experience working in the ICU. The survey consisted of demographic information and 15 multiple-choice questions related to ventilator waveforms. Results were compared between professions using descriptive statistics, and logistic regression (expressed as odds ratios [ORs; 95% CI]) was performed to identify factors associated with high performance, which was defined by a threshold of 60% correct answers. A total of 1,832 professionals from 31 countries or regions completed the survey; 53% of respondents answered ≥ 60% of the questions correctly. The 3 questions with the most correct responses were related to waveforms that demonstrated condensation (90%), pressure overshoot (79%), and bronchospasm (75%). Conversely, the 3 questions with the fewest correct responses were waveforms that demonstrated early cycle leading to double trigger (43%), severe under assistance (flow starvation) (37%), and early/reverse trigger (31%). Factors significantly associated with ≥ 60% correct answers included years of ICU working experience (≥ 10 y, OR 1.6 [1.2-2.0], P < .001), profession (RT, OR 2.8 [2.1-3.7], P < .001), highest degree earned (graduate, OR 1.7 [1.3-2.2], P < .001), workplace (teaching hospital, OR 1.4 [1.1-1.7], P = .008), and prior ventilator waveforms training (OR 1.7 [1.3-2.2], P < .001). Slightly over half respondents correctly identified ≥ 60% of waveforms demonstrating patient-ventilator discordance. High performance was associated with ≥ 10 years of ICU working experience, RT profession, graduate degree, working in a teaching hospital, and prior ventilator waveforms training. Some discordances were poorly recognized across all groups of surveyed professionals.
The interrelation and complementary nature of multi-omics data can provide valuable insights into the intricate molecular mechanisms underlying diseases. However, challenges such as limited sample size, high data dimensionality and differences in omics modalities pose significant obstacles to fully harnessing the potential of these data. The prior knowledge such as gene regulatory network and pathway information harbors useful gene-gene interaction and gene functional module information. To effectively integrate multi-omics data and make full use of the prior knowledge, here, we propose a Multilevel-graph neural network (GNN): a hierarchically designed deep learning algorithm that sequentially leverages multi-omics data, gene regulatory networks and pathway information to extract features and enhance accuracy in predicting survival risk. Our method achieved better accuracy compared with existing methods. Furthermore, key factors nonlinearly associated with the tumor pathogenesis are prioritized by employing two interpretation algorithms (i.e. GNN-Explainer and IGscore) for neural networks, at gene and pathway level, respectively. The top genes and pathways exhibit strong associations with disease in survival analyses, many of which such as SEC61G and CYP27B1 are previously reported in the literature.
The intertidal wetland ecosystem is vulnerable to environmental and anthropogenic stressors. Understanding how the ecological statuses of intertidal wetlands respond to influencing factors is crucial for the management and protection of intertidal wetland ecosystems. In this study, the community characteristics of bacteria, archaea and microeukaryote from Jiangsu coast areas (JCA), the longest muddy intertidal wetlands in the world, were detected to develop a composite microbial index of biotic integrity (CM-IBI) and to explore the influence mechanisms of stresses on the intertidal wetland ecological status. A total of 12 bacterial, archaea and microeukaryotic metrics were determined by range, responsiveness and redundancy tests for the development of ba-IBI, ar-IBI and eu-IBI. The CM-IBI was further developed via three sub-IBIs with weight coefficients 0.40, 0.33 and 0.27, respectively. The CM-IBI (R2 = 0.58) exhibited the highest goodness of fit with the CEI, followed by ba-IBI (R2 = 0.36), ar-IBI (R2 = 0.25) and eu-IBI (R2 = 0.21). Redundancy and random forest analyses revealed inorganic nitrogen (inorgN), total phosphorus (TP) and total organic carbon (TOC) to be key environmental variables influencing community compositions. A conditional reasoning tree model indicated the close associating between the ecological status and eutrophication conditions. The majority of sites with water inorgN<0.67 mg/L exhibited good statuses, while the poor ecological status was observed for inorgN>0.67 mg/L and TP > 0.11 mg/L. Microbial networks demonstrated the interactions of microbial taxonomic units among three kingdoms decreases with the ecological degradation, suggesting a reduced reliability and stability of microbial communities. Multi-level path analysis revealed fishery aquaculture and industrial development as the dominant anthropogenic activities effecting the eutrophication and ecological degradation of the JCA tidal wetlands. This study developed an efficient ecological assessment method of tidal wetlands based on microbial communities, and determined the influence of human activities and eutrophication on ecological status, providing guidance for management standards and coastal development.