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Background: The increased use of digital data in health research demands interdisciplinary collaborations to address its methodological complexities and challenges. This often entails merging the linear deductive approach of health research with the explorative iterative approach of data science. However, there is a lack of structured teaching courses and guidance on how to effectively and constructively bridge different disciplines and research approaches.

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Our commentary proposes the application of directed acyclic graphs (DAGs) in the design of decision-analytic models, offering researchers a valuable and structured tool to enhance transparency and accuracy by bridging the gap between causal inference and model design in medical decision making.The practical examples in this article showcase the transformative effect DAGs can have on model structure, parameter selection, and the resulting conclusions on effectiveness and cost-effectiveness.This methodological article invites a broader conversation on decision-modeling choices grounded in causal assumptions.

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Mental and oral health are interrelated, and problems in one area usually affect the other. This review discusses the complex relationships between oral and mental health, particularly the psychosocial challenges faced by individuals with mental health disorders in maintaining oral hygiene, including stigma, lack of access to care, and financial barriers. It also discusses how psychiatric conditions influence oral health, with regard to issues such as dry mouth, gum disease, and tooth decay, and how poor oral health can aggravate mental well-being.

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Peyronie's disease (PD) is characterized by the formation of fibrotic plaques within the penile connective tissue, leading to abnormal curvature, pain, and erectile dysfunction, profoundly affecting patients' physical and psychological well-being. The Peyronie's Disease Questionnaire (PDQ) is a validated instrument designed to assess key aspects of the disease, including pain, sexual function, and psychosocial impact. This narrative review underscores the importance of translating and culturally adapting the PDQ into Portuguese to enhance its applicability for Portuguese-speaking populations.

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Background: Logistic regression is a useful statistical technique commonly used in many fields like healthcare, marketing, or finance to generate insights from binary outcomes (e.g., sick vs.

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