It is feasible to predict delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) using Artificial intelligence (AI) algorithms, which may offer significant improvements in early diagnosis and patient management. This systematic review and meta-analysis evaluate the efficacy of machine learning (ML) in predicting DCI, aiming to integrate complex clinical data to enhance diagnostic accuracy. We searched PubMed, Scopus, Web of science, and Embase databases without restrictions until June 2024, applying PRISMA guidelines. Out of 1498 studies screened, 10 met our eligibility criteria involving ML approaches in patients with confirmed aSAH. The studies employed various ML algorithms and reported differential ML metrics outcomes. Meta-analysis was performed on eight studies, which resulted in a pooled sensitivity of 0.79 [95% CI: 0.63-0.89], specificity of 0.78[95% CI: 0.68-0.85], positive DLR of 3.54 [95% CI: 2.22-5.64] and the negative DLR of 0.28 [95% CI: 0.15-0.52], diagnostic odds ratio of 12.82 [95% CI: 4.66-35.28], the diagnostic score of 2.55 [95% CI: 1.54-3.56], and the area under the curve (AUC) of 0.85. These findings show significant diagnostic accuracy and demonstrate the potential of ML algorithms to significantly improve the predictability of DCI, implying that ML could impart a significant role on improving clinical decision making. However, variability in methodological approaches across studies shows a need for standardization to realize the full benefits of ML in clinical settings.

High-grade glioma (HGG) is an aggressive brain tumor with poor survival rates. Predicting survival outcomes is critical for personalized treatment planning. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) models, has emerged as a promising approach for enhancing prognostic accuracy in HGG but this study especially focused on the potential of AI in the recurrence of HGG. A systematic review and meta-analysis were conducted to assess the performance of AI-based models in predicting survival outcomes for HGG patients. Relevant studies were retrieved from PubMed, Embase, Scopus, and Web of Science until 2 Dec 2024, using predefined keywords ("High-Grade Glioma", "Survival" and "Machine Learning") without date or language restrictions. Data extraction and quality assessment were performed in accordance with PRISMA and PROBAST guidelines. In this study were included. The pooled diagnostic metric, the area under the curve (AUC), was analyzed using random-effects models. A total of 39 studies with 29 various algorithms and 79,638 patients were included, with 15 studies contributing to the meta-analysis. The most commonly used algorithms were random forest (RF) and logistic regression (LR), which demonstrated robust predictive accuracy. The pooled AUCs for one-year, two-year, three-year and overall survival predictions were 0.816, 0.854, 0.871 and 0.789 respectively. Subgroup analysis revealed that RSF achieved the highest predictive accuracy with an AUC of 0.91 (95% CI: 0.84-0.98), while LR followed with an AUC of 0.89 (95% CI: 0.82-0.96). Models integrating clinical, radiomics, and genetic features consistently outperformed single-data-type models. MRI was the most frequently utilized imaging modality. AI-based models, particularly ML and DL algorithms, show significant potential for improving survival prediction in HGG patients. By integrating multimodal data, these models offer valuable tools for personalized treatment planning, although further validation in prospective, multicenter studies is needed to ensure clinical applicability.

Este artigo discute as percepções de homens jovens (18-29 anos) moradores do Complexo da Maré, no Rio de Janeiro, Brasil, sobre processos de saúde-doença e desigualdades sociais no contexto da pandemia de Covid-19. Os dados foram coletados por meio de entrevistas semiestruturadas, realizadas entre setembro de 2022 e fevereiro de 2023, e analisados usando a análise de conteúdo temática. Os resultados permitem traçar um quadro sobre saúde, desigualdades sociais e território sob a ótica dos próprios sujeitos da pesquisa. Refletindo sobre adesão a ações em saúde, sociabilidade na favela e a vivência cotidiana da violência e do racismo, esses jovens trazem um olhar singular acerca da saúde do homem que se expande para além do período de emergência sanitária.

A atenção à saúde do homem no Brasil permanece um desafio apesar da Política Nacional de Atenção Integral à Saúde do Homem (PNAISH). Este estudo discute as experiências do uso dos serviços pelos homens usuários e relações estabelecidas entre estes e os profissionais médico(a)s em serviços de saúde em Florianópolis, SC. Utilizando pesquisa qualitativa com triangulação de métodos (entrevistas semiestruturadas e grupos focais) em duas Unidades de Saúde da Família, revela-se que a atenção dividida dos médicos e a troca frequente de profissionais impactam negativamente na formação de vínculos. São reconhecidos os benefícios de uma abordagem que atua para além da precisão técnica e destaca-se a importância de estratégias como a manutenção de espaços de troca e escuta. O estudo contribui para a compreensão da implantação da PNAISH na Atenção Primária à Saúde e identifica desafios no cuidado à saúde dos homens.