This study examines social participation as an ethical criterion for validating artificial intelligence (AI) tools in public administration, focusing on its role in fostering transparency and equity. An integrative review of 30 articles (2018–2024), analyzed the literature using Habermas’s communicative action and Rawls’s justice as fairness frameworks, supplemented by decolonial perspectives. The findings confirm that social participation is critical for mitigating algorithmic bias, enhancing transparency, and addressing inequalities in AI systems. However, practical implementation remains limited, with Global North perspectives dominating ethical guidelines and marginalizing Global South voices. This study proposes a hybrid governance model integrating rational dialogue and equity to legitimize AI in public administration. It advocates for digital deliberative forums to amplify marginalized communities and decolonial approaches to address digital and sociocultural disparities. Future research should employ longitudinal case studies and mixed methods to assess the impact of participation on AI ethics in diverse global contexts.

Background: Bioprosthetic mitral valve degeneration is traditionally treated with redo surgical mitral valve replacement (redo‐SMVR), but valve‐in‐valve transcatheter mitral valve replacement (ViV‐TMVR) offers a less invasive alternative. Methods: Systematic review and meta‐analysis of studies comparing ViV‐TMVR and redo‐SMVR. PubMed/MEDLINE, EMBASE, Web of Science, and Cochrane databases (inception to September 2025) were searched. Meta‐analyses were conducted with random‐effects models to assess patient‐relevant outcomes; Kaplan–Meier‐derived time‐to‐event data were pooled to assess late outcomes. Results: Thirteen observational studies met our eligibility criteria, including 15 941 patients (ViV‐TMVR: 5465; redo‐SMVR: 10476). In comparison with redo‐SMVR, ViV‐TMVR was associated with lower risk of in‐hospital mortality (risk ratio [RR], 0.72 [95% CI, 0.57–0.90]; P =0.004), stroke (RR, 0.49 [95% CI, 0.29–0.83]; P =0.008), bleeding (RR, 0.43 [95% CI, 0.20–0.94]; P =0.035), acute kidney injury (RR, 0.57 [95% CI, 0.42–0.77]; P <0.001), permanent pacemaker implantation (RR, 0.30 [95% CI, 0.19–0.49]; P <0.001), and shorter hospital length of stay (mean difference,−5.09 days [95% CI, −6.56 to −3.63]; P <0.001). There was no statistically significant difference between the groups in terms of 5‐year survival (hazard ratio [HR], 0.92 [95% CI, 0.81–1.05]; P =0.256); however, the landmark analysis revealed that ViV‐TMVR was associated with lower risk of death in the initial 6 months (HR, 0.69 [95% CI, 0.58–0.83]; P <0.001) but a higher risk beyond 6 months (HR, 1.47 [95% CI, 1.20–1.79]; P <0.001). Conclusions: In patients amenable to ViV‐TMVR, this procedure shows a lower initial risk of death and complications, but higher mortality after 6 months in comparison with redo‐SMVR. These findings highlight the importance of striking a balance between upfront surgical risk and estimated life expectancy when selecting interventions.

Pituitary tumors are the second most common type of brain tumor in adults, accounting for 17% of adult tumors. Over the years, endoscopic transsphenoidal surgery has become widely used in the treatment of pituitary tumors. With the recent advancement of Artificial Intelligence (AI) and Machine Learning (ML) technologies to generate clinical predictions that aid in diagnostic medicine, they have stood out as preoperative planning tools with the aim of predicting clinical outcomes, including postoperative ones. This article aims to evaluate the applicability of ML models in predicting postoperative complications in the postoperative period of endoscopic transsphenoidal surgeries for pituitary lesions. Following the PRISMA protocol. Articles were included if they had an observational or randomized design, reported on patients with skull base tumors (pituitary adenomas) who underwent transsphenoidal surgery, used machine learning-based prediction for major outcomes after surgery, and reported at least one relevant outcome. Eight articles were included in this analysis. The 2 outcomes analyzed were diabetes insipidus and cerebrospinal fluid (CSF) leak. The models studied were Random Forest, Neural Network, Logistic Regression, and Decision Tree, and they presented sensitivity, specificity, and an average area under the curve greater than 0.7. The best model was Random Forest with an average area under the curve of 0.832, followed by the Neural Network model, which had an average area under the curve of 0.8. In the studies we identified, the best machine learning methodology for identifying patients at risk of Diabetes Insipidus and cerebrospinal fluid leak was Random Forest.

Objective: This scoping review aims to map the scientific literature of the last five years to characterize behavioral patterns in children with snoring and sleep-disordered breathing (SDB), ranging from primary snoring (PS) to obstructive sleep apnea syndrome (OSAS). The review seeks to identify the main diagnostic and assessment methods, differentiate the behavioral findings between PS and OSAS where possible, and pinpoint key research gaps. Methods: A systematic scoping review was conducted following the PRISMA-ScR guidelines. The PubMed/MEDLINE, LILACS, and SciELO databases were searched in April 2024 for observational studies published between 2019 and 2024 that addressed the coexistence of snoring and behavioral changes in the pediatric population. Only articles in English, Spanish, or Portuguese were analyzed. Data were charted and analyzed thematically to map the scope of the evidence. Results: The initial search yielded 129 articles, with 22 ultimately included in the final analysis. The findings reveal profound methodological heterogeneity. While questionnaires are universally applied (n = 22), polysomnography (n = 21) remains the gold standard for SDB diagnosis. Behavioral assessments were inconsistent, identifying a broad spectrum of externalizing (e.g., hyperactivity, aggression) and internalizing (e.g., anxiety, depression) problems, with no clear predominant pattern. Obesity and Down’s syndrome were the most frequently associated comorbidities. Conclusions: The literature reaffirms the strong correlation between SDB and adverse neurobehavioral outcomes in children. This association is present in primary snoring but is most pronounced in children with diagnosed OSAS. However, progress is constrained by a lack of standardization and, critically, a frequent failure to differentiate between PS and OSAS, which hinders clinical interpretation and evidence synthesis.

Objetivo: descrever o perfil da mortalidade por suicídio nos estados da Região Norte do Brasil e analisar a tendência temporal no período de 2018 a 2022. Métodos: Trata-se de um estudo descritivo e retrospectivo, com dados secundários da Região Norte do Painel de Monitoramento da Mortalidade do Departamento de Análise e Vigilância de Agravos Não Transmissíveis em Saúde da Secretaria de Vigilância em Saúde do Ministério da Saúde. Resultados: a análise temporal das taxas de mortalidade por suicídio revelou um crescimento contínuo ao longo do período estudado, com maior taxa em 2022 (7,13). Em 2021, o estado de Roraima registrou a maior taxa de mortalidade por suicídio do período, com 10,53. O Tocantins apresentou a maior média de taxa de mortalidade ao longo dos cinco anos analisados, com um valor de 9. O perfil mais comum das vítimas foi de homens, com idade entre 20 e 39 anos, predominantemente de cor/raça parda. Conclusões: a ampliação da cobertura da Rede de Atenção Psicossocial nos estados da Região Norte é essencial para fortalecer o atendimento à saúde mental, especialmente na atenção básica. É fundamental que esse suporte seja acessível, multidisciplinar e que garanta maior efetividade na prevenção do suicídio.