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The Table 2 Fallacy and Overfitting: A Persistent Problem in Contemporary Research?
Full text linkThe "Table 2 fallacy" represents a common methodological error in medical research, characterized by indiscriminate statistical adjustment for multiple variables without considering their causal nature. This article examines the theoretical foundations of the problem, distinguishing between studies with descriptive, predictive, and explanatory objectives, and emphasizing how the research purpose should determine the adjustment strategy. We highlight the fundamental role of Directed Acyclic Graphs (DAGs) in correctly identifying confounding, mediating, and colliding variables, thus avoiding overadjustment and resulting biases. To illustrate these considerations, we present two practical examples: the relationship between obesity and colorectal cancer, and between coffee consumption and breast cancer. In the first case, we demonstrate how adjustment for intestinal dysbiosis (a mediator) can attenuate the association between obesity and colorectal cancer, reducing the adjusted relative risk from 1.78 (95% CI: 1.20–2.65) to 1.49 (95% CI: 0.97–2.29) and eliminating statistical significance (p=0.072). In the second example, we show how including insomnia (a collider) in the model can create artificial associations between coffee consumption and breast cancer, dramatically increasing the adjusted relative risk to 1.94 (95% CI: 1.34-2.81) with high statistical significance (p<0.001), when a correctly specified model shows no such association. We conclude that, in explanatory studies, it is essential to develop causal reasoning prior to statistical analysis, using DAGs to guide the selection of adjustment variables. This rigorous methodological approach prevents both the dilution of real causal effects and the generation of spurious associations, increasing the internal validity of epidemiological findings and their utility for clinical decision-making
Use of Convolutional Neural Networks for Detection of Pathologies in Dental X-Ray Images in Clinical Decision Support Systems
Full text linkRelevance: The relevance of the study is determined by the need for automated, scalable solutions for processing large volumes of dental radiological images, which provide precise segmentation, detection, and classification of pathologies in the integrated Clinical Decision Support System (CDSS) modules.
Aim: The aim of the study is to develop, optimize, and verify a HITL-CDSS framework for dental radiology with multi-level integration of Convolutional Neural Network (CNN) models, ensuring architectural consistency, metric validity, and expert adaptability.
Methods: Research methods: critical architectural and functional analysis of CNN models, metric and indicator modelling of efficiency, synthesis and Unified Modelling Language-based (UML)modelling of the CDSS framework, UML optimization with Human-in-the-loop (HITL) integration, metric and indicator verification of HITL-CDSS.
Results: Architectural and functional, metric and indicator, as well as UML modelling of CNN architectures was carried out for the purpose of integration into the dental radiology CDSS. The resultant HITL-optimized framework based on DenseNet/EfficientNet, HRNet, YOLOv8 provided AUC = 0.96–0.98, F1@t = 0.91–0.94, DSC = 0.89–0.92, mAP = 0.72–0.77 at ECE = 0.02–0.04. Integration of HITL mechanisms increased Explainable Artificial Intelligence (XAI) interpretability, resistance to domain shifting, and clinical validity, indicating the appropriateness of multi-modular construction of CDSS with the inclusion of expert feedback.
Conclusion: The academic novelty of the study is the development of a HITL-CDSS framework with multi-level CNN integration, which provides metrically verified interpretability, domain-stable generalizability, and clinical relevance in dental radiology tasks
Safety-Oriented Optimization of Polymer Components in FDM Using MCDM
Full text linkFused deposition modeling (FDM) has become a widely adopted additive manufacturing method for producing functional polymer components across industrial and biomedical domains. However, ensuring both mechanical performance and safety reliability remains challenging due to the sensitivity of FDM outcomes to process parameters. This study proposes a decision-making framework integrating Fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to optimize FDM process parameters—layer thickness, infill density, print speed, and extrusion temperature—based on mechanical and safety performance indicators. Experimental and decision analyses identified an optimal configuration of 0.2 mm layer thickness, 80% infill density, 60 mm/s print speed, and 220 °C extrusion temperature, resulting in a 17.6% improvement in tensile strength and a 14.3% increase in safety factor, calculated as the ratio of maximum tensile stress to yield stress, compared to baseline settings. The proposed framework provides a systematic pathway for balancing mechanical integrity and safety reliability in polymer additive manufacturing, offering practical value for industrial optimization and sustainable design
Cognitive Profiles of Mexican Elementary Education Teachers on School Inclusion of Students with Disability
Full text linkThis research investigated the attitudes of 99 elementary education teachers toward the inclusion of students with disabilities in schools. The present authors designed a factorial experiment with 2 (View on learning difficulties: static vs. interactionist) x 2 (Teacher approach: teaching vs. learning) x 2 (Social climate: homogeneous vs. heterogeneous) x 3 (Institutional culture: individualistic vs. delegation vs. collaborative). The orthogonal combination of these factors resulted in 24 experimental conditions ready to build the 24 experimental scenarios of this study. Each scenario described a hypothetical story about the thinking of teachers who faced a school inclusion situation. The experimental task for the participants was to read each scenario individually and then assess their level of identification with the protagonist. The results indicated two types of teacher profiles among the participants: the pre-inclusive profile, which showed a low level of identification towards the inclusive vision, and the inclusive profile, which presented a moderate level of school inclusiveness. The most relevant factors for making inclusive judgments in both groups were the diversity climate (hp2 = 0.24) and professional culture (hp2 = 0.13). However, the pre-inclusive group showed a greater affinity with school homogeneity and individual work. In contrast, the second group showed greater acceptance of diversity and collaboration in school inclusion. In this article, the present authors discuss the implications of these findings within the classroom
AI Applications in Vitagen-Based Education: Expanding Opportunities and Emerging Risks in Developing Students’ Mentality
Full text linkVitagen-based education treats learning not only as the acquisition of disciplinary knowledge, but as the deliberate cultivation of students’ mentality – a constellation of metacognitive, self-regulatory, motivational, and socio-emotional dispositions that shape how individuals relate to themselves, others, and a rapidly changing world. This article explores how artificial intelligence (AI) can both strengthen and destabilise this project. First, it reconstructs the theoretical foundations of vitagen-based education and clarifies the notion of mentality in relation to agency, reflection, and lifelong learning. It then maps four major clusters of AI applications – personalisation and adaptive pathways, intelligent mentoring and feedback, assessment and data-informed insights, and learning analytics for mentality development – showing how these tools can scaffold reflection, support growth mindset, and extend socio-emotional learning when carefully designed. At the same time, the article identifies four interlocking domains of risk: privacy and surveillance; bias, fairness, and inclusivity; dependency and erosion of critical thinking; and mental health and well-being. Drawing on international cases and longitudinal studies, the analysis distils pedagogical and policy implications for curriculum design, teacher professional development, and multi-level governance of AI in education. Finally, it outlines a research agenda for AI-enhanced vitagen education, arguing for ethically grounded, mixed-methods and cross-disciplinary inquiry. The article contends that AI will not automatically elevate vitagen-based education, but, under well-governed conditions, can become a powerful – though never neutral – partner in developing students’ mentality
Reticular Outflow, Rumen Dynamics, and Ingestive Behavior in Buffaloes (Bubalus bubalis) Fed Diets with Different Sources of Energy
Full text linkThe objective of this study was to evaluate the effects of two energy sources, associated or not (crude glycerin and castor oil) in buffaloes' diets on intake, digestibility, rumen kinetics, feeding behavior, nitrogen balance, microbial protein synthesis, rumen fermentation, and blood metabolites. Four ruminally-cannulated Murrah buffaloes [526 ± 29 kg of initial BW] were randomly assigned according to a 4 x 4 Latin square design in which the animals were randomly allocated to each treatment: CONT = control with soybean meal associated with ground corn; GLY = crude glycerin, dietary inclusion of 90 g/kg; CAO = castor oil, dietary inclusion of 50 g/kg; GLYCAO = crude glycerin associated with castor oil, dietary inclusions of 50 g/kg GLY and 50 g/kg CAO. A higher ruminal renewal rate of DM and NDF and DM passage rate was observed for animals fed CON and GLY than the other diets (P < 0.05), the same fact was observed for rumination efficiency grams DM/hour (P < 0.001). Among the feed sources, crude glycerin can partially replace ground corn in the buffalo’s diet without compromising intake, nutrient metabolism, and rumen dynamics
Regional Determinants of Child Mortality in Nigeria: Evidence from Survival Analysis
No full textObjective: This study investigated regional determinants of child mortality in northern Nigeria, focusing on socio-economic, demographic, and environmental factors.
Methods: Data from the 2018 Nigerian Demographic and Health Survey (NDHS) on 10,400 under-five children were analyzed. Kaplan-Meier survival curves and the Cox Proportional Hazards model assessed survival probabilities and risk factors. However, the proportional hazards (PH) assumption was violated (χ² = 1190.00, df = 13, p < 2e-16), indicating time-varying effects. Consequently, the Weibull model was used for a more precise estimation.
Results: Kaplan-Meier estimates revealed significant regional disparities in child survival (p < 0.0001), with North Central having the highest survival, North East intermediate, and North West the lowest. The log-rank test (χ² = 176.214, p < 0.001) confirmed these differences. The Weibull model identified male children as having a higher mortality riskcompared to female children, likely due to biological vulnerabilities and variations in healthcare. Larger households and shorter birth intervals increased mortality risk due to resource constraints. In contrast, improved sanitation and clean water access significantly reduced mortality. Higher maternal education, household wealth, and breastfeeding were strongly associated with better survival. Notably, not breastfed children had a 45%, 46%, and 49% higher mortality risk across regions. Birth intervals exceeding 35 months and maternal age at first birth between 29 and 36 years improved survival.
Conclusion: These findings emphasized the need for policy interventions, including family planning, improved sanitation, maternal education, and breastfeeding promotion, to reduce child mortality and regional disparities in Nigeria
Innovative SiKaRen Smartphone Application Model: A Breakthrough in Enhancing IMP Cadres’ Knowledge and Attitudes Toward Family Planning
Full text linkObjectives: There are several challenges in the implementation of family planning programs in urban areas, one of which is to enhance the capacity of the urban community institutions (IMPs) as the first level of family counselling and support. The other major constraint is the limited availability of new and efficient training techniques, because conventional training is not very efficient in enhancing the knowledge and beliefs of IMP cadres. In the digital era, the use of smartphone-based technology could be the solution to improve the effectiveness of cadre training in a more flexible and interactive way. This study was conducted to assess the efficacy of the smartphone based SiKaRen application in enhancing the knowledge and attitudes of IMP cadres.
Methods: This study used a mixed-methods design, combining quantitative and qualitative approaches. The quantitative aspect employed a quasi-experimental design with pre- and post-tests on two groups: an intervention group using the SiKaRen Applications and a control group with conventional training. The qualitative approach explored the roles, knowledge, and attitudes of 10 Informants through in-depth interviews, focus group discussions (FGDs), and standardized questionnaires. Data were analyzed using statistical tests.
Results: The problem of the cadres was that they did not have the required knowledge about the advantages, disadvantages and side effects of various contraceptive methods, hence lacked the confidence of providing advice. When they encounter challenges, they just quit, but they do attempt to help and look for assistance. Furthermore, the role of the cadres is not optimal due to limited facilities, not clearly defined functions, missing documents and lack of innovation. The SiKaRen model based on a smartphone was found to have a significant effect in enhancing the knowledge and attitude of the cadres in the field (p-value < 0.05) and therefore could be a way of solving the problems faced by cadres in the field.
Conclusions: The integration of technology into the SiKaRen model enhances the ability of cadres to receive the latest information and to track and monitor family planning participants more effectively. This digital application also enables more precise interventions based on accurate data, meaning that cadres are not only facilitators, but also drivers of family planning awareness
Response Adaptive Randomization Using Biomarkers with Exponentially Decreasing Probability Sequence
Full text linkIn this article, it is proposed to study the application of Response Adaptive Randomization (RAR) design in clinical trials. The approach involves the prediction of treatment outcomes based on the biomarker of patients using a regression model. The focus is on rare diseases to efficiently allot the patients among various treatments so as to ensure not only the clinical rights but also the maximum possible benefits to the patients even when they are in clinical trials. Initially, the method uses conventional equal randomization to understand how well every treatment works in patients and this initial duration is known as burn-in period. The proposed work allocates patients to treatments by using an exponentially decreasing probability sequence instead of the existing linearly decreasing sequence to have higher allocation probability to the efficient treatment. In the case of rare disease, it is observed from simulation study that the use of exponentially decreasing probability sequence in RAR design increases the benefit to the patients in the clinical trials when compared to the existing method that uses linearly decreasing sequence. The study also investigates the performance of the proposed RAR design when used with different regression methods under various scenarios. The performance of the proposed design is measured by the proportion of patients assigned to the best treatment in addition to Type I error and power. From the impressive results, it is suggested that the proposed RAR design can be implemented practically in clinical trials of rare diseases without any apprehension
Feeding Practices and Nutritional Status of Infants and Young Children Aged 6-23 Months in the South Kivu Region: A Cross-Sectional Study
Full text linkBackground: The increasing prevalence of stunting in the Democratic Republic of the Congo (DRC) represents a major public health concern. Adequate complementary feeding is crucial for preventing and reducing chronic malnutrition in early childhood in the long term.
Objective: This study aimed to assess the baseline complementary feeding practices and nutritional status of children aged 6–23 months in South Kivu Province, with a focus on commonly consumed complementary foods.
Methods: A cross-sectional analytical study was conducted among 515 children in the Kadutu and Miti-Murhesa health zones of South Kivu. Participants were selected through a systematic random sampling method. Dietary intakes and infant feeding practices were assessed using dietary recall questionnaires and a 7-day food frequency questionnaire. The nutritional status was measured by anthropometry.
Results: The mean age of children was 13.3 ± 5 months. Results showed that 59% of the children had a low dietary diversity score. Only 23% received an appropriate complementary feeding according to the minimum acceptable diet. Most of the children (88.5%) consumed porridge made exclusively of cereals, roots, or tubers and water. Animal-source foods, fruits, and vegetables were rarely consumed. Acute malnutrition and stunting affected 4.9% and 36.6% of children, respectively.
Conclusion: Stunting remains prevalent in both rural and urban areas of South Kivu. Furthermore, infant diets are nutritionally inadequate, as evidenced by their lack of diversity. Enriching widely consumed staple foods (maize, sorghum, and soy) with locally available animal-source products could improve micronutrient intake and constitute a promising strategy for preventing child malnutrition