Browsing by Author "Fernandes, C."
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- Estudo neurofisiológico do jogo patológicoPublication . Dores, Artemisa R.; Fernandes, R.; Geraldo, Andreia,; Macedo, I.; Barbosa, F.; Fernandes, C.O jogo patológico é uma dependência comportamental, segundo a Classificação Internacional de Doenças (CDI-11, WHO, 2019) e o Manual de Diagnóstio e Estatística das Perturbações Mentais (DSM-5; APA, 2013). Consiste num comportamento recorrente de jogo, apesar das consequências negativas em diversas áreas da vida do individuo. Nas últimas décadas diversos investigadores têm procurado identificar possíveis variáveis explicativas deste comportamento. De entre elas destaca-se o efeito de “near-win” [quase-ganho], que se refere a um resultado de perda, percecionado pelos jogadores como estando próximo do ganho. Um dos métodos utilizados no seu estudo é a eletroencefalografia (EEG), por permitir estudar a resposta neuronal aos diferentes tipos de resultado de uma jogada (i.e., ganho, perda ou casoganho).
- Validity of central pain processing biomarkers for predicting the occurrence of oncological chronic pain: a study protocolPublication . Carrillo‑de‑la‑Peña, M. T.; Fernandes, C.; Castro, Catarina; Medeiros, R.Despite recent improvements in cancer detection and survival rates, managing cancer-related pain remains a significant challenge. Compared to neuropathic and inflammatory pain conditions, cancer pain mechanisms are poorly understood, despite pain being one of the most feared symptoms by cancer patients and significantly impairing their quality of life, daily activities, and social interactions. The objective of this work was to select a panel of biomarkers of central pain processing and modulation and assess their ability to predict chronic pain in patients with cancer using predictive artificial intelligence (AI) algorithms. We will perform a prospective longitudinal cohort, multicentric study involving 450 patients with a recent cancer diagnosis. These patients will undergo an in-person assessment at three different time points: pretreatment, 6 months, and 12 months after the first visit. All patients will be assessed through demographic and clinical questionnaires and self-report measures, quantitative sensory testing (QST), and electroencephalography (EEG) evaluations. We will select the variables that best predict the future occurrence of pain using a comprehensive approach that includes clinical, psychosocial, and neurophysiological variables. This study aimed to provide evidence regarding the links between poor pain modulation mechanisms at precancer treatment in patients who will later develop chronic pain and to clarify the role of treatment modality (modulated by age, sex and type of cancer) on pain. As a final output, we expect to develop a predictive tool based on AI that can contribute to the anticipation of the future occurrence of pain and help in therapeutic decision making.