Search Results (23)

Background/Objectives: Diabetes is major metabolic disorder and rapidly increasing public health problem globally. The greatest way to reduce diabetic complications is adequate knowledge about the condition. Hence, the primary objectives of this study were to evaluate the psychometric properties of the Simplified Diabetes Knowledge Test—Arabic version (SDKT-A) among Iraqi insulin-dependent diabetic patients. Additionally, the secondary objectives were to assess the associated independent variables and the risk of atherosclerosis and cardiovascular risk event by using atherogenic indices and lipid ratios with the SDKT-A. Methods: A cross-sectional, descriptive study was conducted in primary healthcare clinics. The SDKT was translated into Arabic using forward–backward translation, reconciliation, and pilot testing. Thereafter, psychometric properties of the SDKT-A were evaluated depending on different criteria. Atherogenic indices of Castelli risk indices I and II (CRI-I and II), triglyceride/HDL ratio, non-HDL-C ratio, atherogenic coefficient (AC), and triglyceride–total cholesterol–body weight index (TCBI) were calculated using specific formulas. Results: The SDKT-A questionnaire showed acceptable readability and validity. Cronbach’s alpha test (95% confidence interval) was 0.662 (0.59–0.73). The Pearson correlation coefficient of reliability for test–retest was found to be 0.659. The item difficulty index for most items was between 0.237 and 0.877. The point biserial correlation values ranged from 0.028 to 0.535 with Ferguson’s sigma value equal to 0.962. The content validation results showed a significant content validity ratio (CVR) value for most of the questions, ranging from 0.8 to 1. The content validity index (CVI) value for SDKT-A was found to be 0.98, which showed good agreement between experts. In addition, the exploratory factor analysis with promax rotation identified four domains for the final 20 items of the SDKT-A that explained 41.83% of the scale total variance. The mean score of the SDKT-A was 11.09 ± 3.40. The total score of the SDKT-A was positively and significantly correlated with education level (r = 0.322, p < 0.01). In addition, the total scores of the SDKT-A were negatively and significantly correlated with glycemic control, age, CRI-I, CRI-II, triglyceride/HDL ratio, AC, non-HDL-C ratio, and TCBI. Furthermore, the glycemic control (HbA1c) was positively and significantly correlated with the preventive measures factor (r = 0.175, p < 0.05), and were negatively and significantly correlated with the lifestyle and modification factor (r = −0.169, p < 0.05), diet and monitoring factor (r = −0.158, p < 0.05), and awareness factor (r = −0.149, p < 0.05). Conclusions: This study showed acceptable psychometric properties for the SDKT-A, with low levels of knowledge of diabetic disease in the sample population. Finally, comprehensive and interactive educational programs regarding lifestyle and modification, diet, and monitoring and awareness in primary healthcare centers in Iraq are warranted. Full article

This study aims to assess the capability of Large Language Models (LLMs), particularly GPT-4o, to evaluate and modify the complexity level of Russian school textbooks. We lay the groundwork for developing scalable, context-aware methods for readability assessment and text simplification in Russian educational materials, areas where traditional formulas often fall short. Using a corpus of 154 textbooks covering various subjects and grade levels, we evaluate the extent to which LLMs accurately predict the appropriate comprehension level of a text and how well they simplify texts by targeted grade reduction. Our evaluation framework employs GPT-4o as a multi-role agent in three distinct experiments. First, we prompt the model to estimate the target comprehension age for each segment and identify five key linguistic or conceptual features underpinning its assessment. Second, we simulate student comprehension by instructing the model to reason step-by-step through whether the text is understandable for a hypothetical student of the given grade. Third, we examine the model’s ability to simplify selected fragments by reducing their complexity by three grade levels. We further measure model perplexity and output token probabilities to probe the prediction confidence and coherence. Results indicate that while LLMs show considerable potential in complexity assessment (i.e., MAE of 1 grade level), they tend to overestimate text difficulty and face challenges in achieving precise simplification levels. Ease of understanding assessments generally align with human expectations, although texts with abstract, technical, or poetic content (e.g., Physics, History, and Literary Russian) pose challenges. Our study concludes that LLMs can substantially complement traditional readability metrics and assist teachers in developing suitable Russian educational materials. Full article

Objectives: This study aimed to evaluate the performance of three widely used artificial intelligence (AI) chatbots—ChatGPT-4, Gemini 2.5 Pro, and Claude Sonnet 4—in answering patient-oriented frequently asked questions (FAQs) related to orthognathic surgery. Given the increasing reliance on AI tools in healthcare, it is essential to evaluate their performance to provide accurate, empathetic, readable, and clinically appropriate information. Methods: Twenty FAQs in Turkish about orthognathic surgery were presented to each chatbot. The responses were evaluated by three oral and maxillofacial surgeons using a modified Global Quality Score (GQS), binary clinical appropriateness judgment, and a five-point empathy rating scale. The evaluation process was conducted in a double-blind manner. The Ateşman Readability Formula was applied to each response using an automated Python-based script. Comparative statistical analyses—including ANOVA, Kruskal–Wallis, and post hoc tests—were used to determine significant differences in performance among chatbots. Results: Gemini outperformed both GPT-4 and Claude in GQS, empathy, and clinical appropriateness (p < 0.001). GPT-4 demonstrated the highest readability scores (p < 0.001) but frequently lacked empathetic tone and safety-oriented guidance. Claude showed moderate performance, balancing ethical caution with limited linguistic clarity. A moderate positive correlation was found between empathy and perceived response quality (r = 0.454; p = 0.044). Conclusions: AI chatbots vary significantly in their ability to support surgical patient education. While GPT-4 offers superior readability, Gemini provides the most balanced and clinically reliable responses. These findings underscore the importance of context-specific chatbot selection and continuous clinical oversight to ensure safe and ethical AI-driven communication. Full article

Readability formulas are mathematical functions that assess the ‘difficulty’ level of a given text. They play a crucial role in aligning educational texts with student reading abilities; however, existing models are often not tailored to specific linguistic or regional contexts. This study aims to develop and evaluate two novel readability formulas specifically designed for the Mexican Spanish language, targeting elementary education levels. The formulas were trained on a corpus of 540 texts drawn from official elementary-level textbooks issued by the Mexican public education system. The first formula was constructed using multiple linear regression, emulating the structure of traditional readability models. The second was derived through genetic programming (GP), a machine learning technique that evolves symbolic expressions based on training data. Both approaches prioritize interpretability and use standard textual features, such as sentence length, word length, and lexical and syntactic complexity. Experimental results show that the proposed formulas outperform several well-established Spanish and non-Spanish readability formulas in distinguishing between grade levels, particularly for early and intermediate stages of elementary education. The GP-based formula achieved the highest alignment with target grade levels while maintaining a clear analytical form. These findings underscore the potential of combining machine learning with interpretable modeling techniques and highlight the importance of linguistic and curricular adaptation in readability assessment tools. Full article

Chronobiotics represent a pharmacologically diverse group of substances, encompassing both experimental compounds and those utilized in clinical practice, which possess the capacity to modulate the parameters of circadian rhythms. These substances influence fluctuations in various physiological and biochemical processes, including the expression of core “clock” genes in model organisms and cell cultures, as well as the expression of clock-controlled genes. Despite their chemical heterogeneity, chronobiotics share the common ability to alter circadian dynamics. The concept of chronobiotic drugs has been recognized for over five decades, dating back to the discovery and detailed clinical characterization of the hormone melatonin. However, the field remains fragmented, lacking a unified classification system for these pharmacological agents. The current categorizations include natural chrononutrients, synthetic targeted circadian rhythm modulators, hypnotics, and chronobiotic hormones, yet no comprehensive repository of knowledge on chronobiotics exists. Addressing this gap, the development of the world’s first curated and continuously updated database of chronobiotic drugs—circadian rhythm modulators—accessible via the global Internet, represents a critical and timely objective for the fields of chronobiology, chronomedicine, and pharmacoinformatics/bioinformatics. The primary objective of this study is to construct a relational database, ChronobioticsDB, utilizing the Django framework and PostGreSQL as the database management system. The database will be accessible through a dedicated web interface and will be filled in with data on chronobiotics extracted and manually annotated from PubMed, Google Scholar, Scopus, and Web of Science articles. Each entry in the database will comprise a detailed compound card, featuring links to primary data sources, a molecular structure image, the compound’s chemical formula in machine-readable SMILES format, and its name according to IUPAC nomenclature. To enhance the depth and accuracy of the information, the database will be synchronized with external repositories such as ChemSpider, DrugBank, Chembl, ChEBI, Engage, UniProt, and PubChem. This integration will ensure the inclusion of up-to-date and comprehensive data on each chronobiotic. Furthermore, the biological and pharmacological relevance of the database will be augmented through synchronization with additional resources, including the FDA. In cases of overlapping data, compound cards will highlight the unique properties of each chronobiotic, thereby providing a robust and multifaceted resource for researchers and practitioners in the field. Full article

Background/Objectives: The wording of informed consent forms for medical or surgical clinical procedures can be difficult to read and comprehend, making it difficult for patients to make decisions. The objective of this study was to analyze the readability of informed consent forms for medical or surgical clinical procedures. Methods: A systematic review was performed according to the PRISMA statement using PubMed, Embase, and Google Scholar databases. Primary studies analyzing the readability of informed consent forms using mathematical formulas published in any country or language during the last 10 years were selected. The results were synthesized according to the degree of reading difficulty to allow for the comparison of the studies. Study selection was performed independently by the reviewers to avoid the risk of selection bias. Results: Of the 664 studies identified, 26 studies were selected that analyzed the legibility of 13,940 forms. Of these forms, 76.3% had poor readability. Of the six languages analyzed, only English, Spanish, and Turkish had adapted readability indexes. Flesch Reading Ease was the most widely used readability index, although it would be more reliable to use language-specific indices. Conclusions: Most of the analyzed informed consent forms had poor readability, which made them difficult for a large percentage of patients to read and comprehend. It is necessary to modify these forms to make them easier to read and comprehend, to adapt them to each specific language, and to carry out qualitative studies to find out the real legibility of each specific population. Full article

This publication examines the representation of information within test specifications and formulas defined in standards and directives. This information often pre-defines not only the tests and requirements to be conducted but also the information backflow within the execution. These results are crucial for the effective management of knowledge throughout the product development process as well as for the creation and maintenance of digital representations of a physical product or plant. However, the accessibility of this information is frequently hindered by its extensive and heterogenous definition across a multitude of standards, directives, and other technical regulations. Furthermore, the pre-defined information is typically documented and processed manually on a recurring basis. Given this challenge, the following article presents a holistic two-part approach for pre-defining the information backflow of subsequent physical instances. Initially, an analysis of multiple test specifications in standards and directives is conducted, resulting in the development of a generic data model to represent this Pre-defined Information Backflow (PdIB). The second step builds on the first and defines an optimized representation for machine readability and executability for the future design of standards and directives. The two parts are brought together and validated using representative examples, thereby demonstrating the practical applicability and effectiveness of the proposed approach. This enhances the accessibility and usability of information in test specifications and formulas, thereby establishing a foundation for enhancing the efficiency of knowledge work in product development and the creation of digital representations of products and plants. Full article

Background/Objectives: Artificial intelligence (AI), particularly natural language processing (NLP) models such as ChatGPT, presents novel opportunities for patient education and informed consent. This study evaluated ChatGPT’s use as a support tool for informed consent before penile prosthesis implantation (PPI) in patients with erectile dysfunction (ED) following radical prostatectomy. Methods: ChatGPT-4 answered 20 frequently asked questions across four categories: ED and treatment, PPI surgery, complications, and postoperative care. Three senior urologists independently rated information quality using the DISCERN instrument on a Likert scale ranging from 1 (poor quality) to 5 (good quality). Readability was assessed using the Flesch Reading Ease (FRE) and Flesch–Kincaid Grade Level (FKGL) formulas, and inter-rater reliability was measured using intraclass correlation coefficients. Results: The inter-rater reliability coefficient was 0.76 (95% CI 0.71–0.80). Mean DISCERN scores indicated moderate quality: 2.79 ± 0.92 for ED and treatment, 2.57 ± 0.98 for surgery, 2.65 ± 0.86 for complications, and 2.74 ± 0.90 for postoperative care. High scores (>4) were achieved for clarity and relevance, while complex issues, such as risks and alternative treatments, scored the lowest (<2). The FRE scores ranged from 9.8 to 28.39, and FKGL scores ranged from 14.04 to 17.41, indicating complex readability suitable for college-level comprehension. Conclusions: ChatGPT currently provides variable and often inadequate quality information without sufficient comprehensibility for informed patient decisions, indicating the need for further improvements in quality and readability. Full article

Linear temporal logic (LTL) formalism can ensure the correctness of mobile robot planning through concise, readable, and verifiable mission specifications. For uneven terrain, planning must consider motion constraints related to asymmetric slope traversability and maneuverability. However, even though model checker tools like the open-source Simple Promela Interpreter (SPIN) include search optimization techniques to address the state explosion problem, defining a global LTL property that encompasses both mission specifications and motion constraints on digital elevation models (DEMs) can lead to complex models and high computation times. In this article, we propose a system model that incorporates a set of uncrewed ground vehicle (UGV) motion constraints, allowing these constraints to be omitted from LTL model checking. This model is used in the LTL synthesizer for path planning, where an LTL property describes only the mission specification. Furthermore, we present a specific parameterization for path planning synthesis using a SPIN. We also offer two SPIN-efficient general LTL formulas for representative UGV missions to reach a DEM partition set, with a specified or unspecified order, respectively. Validation experiments performed on synthetic and real-world DEMs demonstrate the feasibility of the framework for complex mission specifications on DEMs, achieving a significant reduction in computation cost compared to a baseline approach that includes a global LTL property, even when applying appropriate search optimization techniques on both path planners. Full article

For the hypothetical reconstruction of architectural heritage, there are still no scientific standards of reference concerning their sharing and documentation. Recent international initiatives established the basis to address this problem; however, still, much work needs to be done in order to systematise good practices for the process of reconstruction and its dissemination. This contribution aims to take a step forward in the analysis and visualisation of uncertainty. Some authors have suggested various approaches to visualise uncertainty for single buildings; however, case studies at the urban scale are rarely investigated. This research proposes an improved source-based multi-feature approach aimed at analysing and visualising (through false-colour shading) the uncertainty of hypothetical 3D digital models of urban areas. The assessment of uncertainty is also quantified qualitatively by using newly defined formulas which calculate the average uncertainty weighted on the volume of the 3D model. This methodology aims to improve the objectiveness, unambiguity, transparency, reusability, and readability of hypothetical reconstructive 3D models, and its use is exemplified in the case study of the hypothetical reconstruction of Piazza delle Erbe in Verona, a project presented in the form of a docufilm at EXPO 2015 in Milan. Full article

The aim of our study was to evaluate the potential role of Artificial Intelligence tools like ChatGPT in patient education. To do this, we assessed both the quality and readability of information provided by ChatGPT 3.5 and 4 in relation to Anterior Cruciate Ligament (ACL) injury and treatment. ChatGPT 3.5 and 4 were used to answer common patient queries relating to ACL injuries and treatment. The quality of the information was assessed using the DISCERN criteria. Readability was assessed with the use of seven readability formulae: the Flesch–Kincaid Reading Grade Level, the Flesch Reading Ease Score, the Raygor Estimate, the SMOG, the Fry, the FORCAST, and the Gunning Fog. The mean reading grade level (RGL) was compared with the recommended 8th-grade reading level, the mean RGL among adults in America. The perceived quality and mean RGL of answers given by both ChatGPT 3.5 and 4 was also compared. Both ChatGPT 3.5 and 4 yielded DISCERN scores suggesting “good” quality of information, with ChatGPT 4 slightly outperforming 3.5. However, readability levels for both versions significantly exceeded the average 8th-grade reading level for American patients. ChatGPT 3.5 had a mean RGL of 18.08, while the mean RGL of ChatGPT 4 was 17.9, exceeding the average American reading grade level by 10.08 grade levels and 9.09 grade levels, respectively. While ChatGPT can provide both reliable and good quality information on ACL injuries and treatment options, the readability of the content may limit its utility. Additionally, the consistent lack of source citation represents a significant area of concern for patients and clinicians alike. If AI is to play a role in patient education, it must reliably produce information which is accurate, easily comprehensible, and clearly sourced. Full article

Timely and relevant information enables clinicians to make informed decisions about patient care outcomes. However, discovering related and understandable information from the vast medical literature is challenging. To address this problem, we aim to enable the development of search engines that meet the needs of medical practitioners by incorporating text difficulty features. We collected a dataset of 209 scientific research abstracts from different medical fields, available in both English and German. To determine the difficulty aspects of readability and technical level of each abstract, 216 medical experts annotated the dataset. We used a pre-trained BERT model, fine-tuned to our dataset, to develop a regression model predicting those difficulty features of abstracts. To highlight the strength of this approach, the model was compared to readability formulas currently in use. Analysis of the dataset revealed that German abstracts are more technically complex and less readable than their English counterparts. Our baseline model showed greater efficacy than current readability formulas in predicting domain-specific readability aspects. Conclusion: Incorporating these text difficulty aspects into the search engine will provide healthcare professionals with reliable and efficient information retrieval tools. Additionally, the dataset can serve as a starting point for future research. Full article

The current manual reading of substation pointer meters wastes human resources, and existing algorithms have limitations in accuracy and robustness for detecting various pointer meters. This paper proposes a method for recognizing pointer meters based on Yolov7 and Hough transform to improve their automatic readability. The proposed method consists of three main contributions: (1) Using Yolov7 object detection technology, which is the latest Yolo technology, to enhance instrument recognition accuracy. (2) Providing a formula for calculating the angle of a square pointer meter after Hough transformation. (3) Applying OCR recognition to the instrument dial to obtain the model and scale value. This information helps differentiate between meter models and determine the measuring range. Test results demonstrate that the proposed algorithm achieves high accuracy and robustness in detecting different types and ranges of instruments. The map of the Yolov7 model on the instrument dataset is as high as 99.8%. Additionally, the accuracy of pointer readings obtained using this method exceeds 95%, indicating promising applications for a wide range of scenarios. Full article

We propose a universal readability index, $G_U$, applicable to any alphabetical language and related to cognitive psychology, the theory of communication, phonics and linguistics. This index also considers readers’ short-term-memory processing capacity, here modeled by the word interval $I_P$, namely, the number of words between two interpunctions. Any current readability formula does not consider $I_p$, but scatterplots of $I_p$ versus a readability index show that texts with the same readability index can have very different $I_p$, ranging from 4 to 9, practically Miller’s range, which refers to 95% of readers. It is unlikely that $I_P$ has no impact on reading difficulty. The examples shown are taken from Italian and English Literatures, and from the translations of The New Testament in Latin and in contemporary languages. We also propose an extremely compact formula, relating the capacity of human short-term memory to the difficulty of reading a text. It should synthetically model human reading difficulty, a kind of “footprint” of humans. However, further experimental and multidisciplinary work is necessary to confirm our conjecture about the dependence of a readability index on a reader’s short-term-memory capacity. Full article

The public demand for popular science knowledge regarding health is increasing, and physicians’ popular science practices on online medical platforms are becoming frequent. Few studies have been conducted to address the relationship between specific characteristics of popular science articles by physicians and their performance. This study explored the impact of the characteristics of popular science articles on physicians’ performance based on the elaboration likelihood model (ELM) from the central path (topic focus and readability) and the peripheral path (form diversity). Data on four diseases, namely, lung cancer, brain hemorrhage, hypertension, and depression, were collected from an online medical platform, resulting in relevant personal data from 1295 doctors and their published popular science articles. Subsequently, the independent variables were quantified using thematic analysis and formula calculation, and the research model and hypotheses proposed in this paper were verified through empirical analysis. The results revealed that the topic focus, readability, and form diversity of popular science articles by physicians had a significant positive effect on physicians’ performance. This study enriches the research perspective on the factors influencing physicians’ performance, which has guiding implications for both physicians and platforms, thereby providing a basis for patients to choose physicians and enabling patients to receive popular science knowledge regarding health in an effective manner. Full article