[strategies For Integration Of Health Care Practices And Sanitary Surveillance In The Context Of The Implementation Of Rede Cegonha - A Brazilian Mother And Infant Health Care Network].

Mother and infant mortality has been the scope of analysis throughout the history of public health in Brazil and various strategies to tackle the issue have been proposed to date. The Ministry of Health has been working on this and the Rede Cegonha strategy is the most recent policy in this context. Given the principle of comprehensive health care and the structure of the Unified Health System in care networks, it is necessary to ensure the integration of health care practices, among which are the sanitary surveillance actions (SSA). Considering that the integration of health care practices and SSA can contribute to reduce mother and infant mortality rates, this article is a result of qualitative research that analyzed the integration of these actions in four cities in the State of São Paulo/Brazil: Campinas, Indaiatuba, Jaguariúna and Santa Bárbara D'Oeste. The research was conducted through interviews with SSA and maternal health managers, and the data were evaluated using thematic analysis. The results converge with other studies, identifying the isolation of health care practices and SSA. The insertion of SSA in collectively-managed areas appears to be a potential strategy for health planning and implementation of actions in the context under scrutiny.

Comparison Between Automated And Microscopic Analysis In Body Fluids Cytology.

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Motivating Medical Students To Learn Basic Science Concepts Using Chronic Myeloid Leukemia As An Integration Theme.

To report on the use of chronic myeloid leukemia as a theme of basic clinical integration for first year medical students to motivate and enable in-depth understanding of the basic sciences of the future physician. During the past thirteen years we have reviewed and updated the curriculum of the medical school of the Universidade Estadual de Campinas. The main objective of the new curriculum is to teach the students how to learn to learn. Since then, a case of chronic myeloid leukemia has been introduced to first year medical students and discussed in horizontal integration with all themes taught during a molecular and cell biology course. Cell structure and components, protein, chromosomes, gene organization, proliferation, cell cycle, apoptosis, signaling and so on are all themes approached during this course. At the end of every topic approached, the students prepare in advance the corresponding topic of clinical cases chosen randomly during the class, which are then presented by them. During the final class, a paper regarding mutations in the abl gene that cause resistance to tyrosine kinase inhibitors is discussed. After each class, three tests are solved in an interactive evaluation. The course has been successful since its beginning, 13 years ago. Great motivation of those who participated in the course was observed. There were less than 20% absences in the classes. At least three (and as many as nine) students every year were interested in starting research training in the field of hematology. At the end of each class, an interactive evaluation was performed and more than 70% of the answers were correct in each evaluation. Moreover, for the final evaluation, the students summarized, in a written report, the molecular and therapeutic basis of chronic myeloid leukemia, with scores ranging from 0 to 10. Considering all 13 years, a median of 78% of the class scored above 5 (min 74%-max 85%), and a median of 67% scored above 7. Chronic myeloid leukemia is an excellent example of a disease that can be used for clinical basic integration as this disorder involves well known protein, cytogenetic and cell function abnormalities, has well-defined diagnostic strategies and a target oriented therapy.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.