Medicina física e reabilitação em doentes com dor crônica

Physical medicine and rehabilitaiton in chronic pain patients physical medicine and rehabilitation including physical procedures, kinesiotherapy, prescription of proteses and orteses and educative programs are necessary and effective in the treatment of chronic muscle-skeletal pain patients. They allow faster and more appropriate rehabilitation and reintegration of the patients.Os procedimentos de medicina física incluindo os meios físicos, a cinesioterapia, o uso de órteses e próteses, as imobilizações,a reabilitação psicossocial e os programas educativos são eficazes e seguros no tratamento da dor e na reabilitação dosdoentes com dor. Proporcionam também reabilitação mais rápida e reintegração mais apropriada dos doentes

Distúrbios ósteo-musculares relacionados ao trabalho

Pain, specially in the upper limbs, resulting from cumulative trauma disorders is very common. Usually results frommuscle-skeletal or neurological lesions. Mechanical and psychic stressores are the causative factors of these conditions. The causal nexus between the clinical entities and the work places conditions is necessary, for the diagnoses . The elimination of the casual factors, the reorganization of the work, the professional readaptation, physical medicine, pharmaceutical agents and psychotherapy are efficient when the lesions were not structuredDor, especialmente nos membros superiores relacionada às atividades laborais, é condição comum. Decorre geralmentede anormalidades músculo-esqueléticas e neurais. Além das condições mecânicas admite-se que a tensão emocional esteja relacionada à sua instalação e manutenção. Não basta apenas haver a identificação da condição clínica, mas é necessário haver nexo entre ela e a condição ambiental causal. A eliminação dos fatores desencadeantes, a reformulação do ambiente e da organização do trabalho, a readaptação profissional, medidas de medicina física e psicocomportamental quando aplicados com critério, melhora na maioria dos casos em que as lesões não estão estruturadas ou irreversíveis

Prediction of Early TBI Mortality Using a Machine Learning Approach in a LMIC Population.

Background: In a time when the incidence of severe traumatic brain injury (TBI) is increasing in low- to middle-income countries (LMICs), it is important to understand the behavior of predictive variables in an LMIC's population. There are few previous attempts to generate prediction models for TBI outcomes from local data in LMICs. Our study aim is to design and compare a series of predictive models for mortality on a new cohort in TBI patients in Brazil using Machine Learning. Methods: A prospective registry was set in São Paulo, Brazil, enrolling all patients with a diagnosis of TBI that require admission to the intensive care unit. We evaluated the following predictors: gender, age, pupil reactivity at admission, Glasgow Coma Scale (GCS), presence of hypoxia and hypotension, computed tomography findings, trauma severity score, and laboratory results. Results: Overall mortality at 14 days was 22.8%. Models had a high prediction performance, with the best prediction for overall mortality achieved through Naive Bayes (area under the curve = 0.906). The most significant predictors were the GCS at admission and prehospital GCS, age, and pupil reaction. When predicting the length of stay at the intensive care unit, the Conditional Inference Tree model had the best performance (root mean square error = 1.011), with the most important variable across all models being the GCS at scene. Conclusions: Models for early mortality and hospital length of stay using Machine Learning can achieve high performance when based on registry data even in LMICs. These models have the potential to inform treatment decisions and counsel family members. Level of evidence: This observational study provides a level IV evidence on prognosis after TBI

IDH1 mutations in a Brazilian series of Glioblastoma

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Albert Einstein Jewish HospitalUniversidade de São Paulo Faculdade de Medicina Department of NeurologyUniversidade de São Paulo Faculdade de Medicina Department of PathologyCancer Institute of São PauloFundação Pio XII Barretos Cancer HospitalFederal University of São Paulo School of Medicine Department of NeurologyFederal University of São Paulo School of Medicine Department of PathologyNove de Julho HospitalAlbert Einstein Jewish HospitalUNIFESP, EPM, Department of NeurologyUNIFESP, EPM, Department of PathologyFAPESP: 04/12133-6SciEL