Critical thinking and accuracy of nurses' diagnoses. Part II: application of cognitive skills and guidelines for self-development

Part I of this article, the author explained the difficulties of achieving accuracy of nurses' diagnoses, the relevance of critical thinking to the achievement of accuracy, and newer views of critical thinking. In Part II, the critical thinking dimensions identified as important for nursing practice are applied in the diagnostic process using a case study of a 16 year old girl with type 1 diabetes. Application of seven cognitive skills and ten habits of mind illustrate the importance of using critical thinking for accuracy of nurses' diagnoses. Ten strategies are proposed for self-development of critical thinking abilities.

Adaptação para a língua portuguesa e validação do Lunney Scoring Method for Rating Accuracy of Nursing Diagnoses

O Lunney Scoring Method for Rating Accuracy of Nursing Diagnoses (LSM) é uma escala de diferencial semântico que foi desenvolvida por Lunney para estimar a acurácia dos diagnósticos de enfermagem. O objetivo deste estudo foi adaptar o LSM para a língua portuguesa e avaliar as sua propriedades psicométricas. A escala original foi traduzida para o português, revertida para o inglês e as duas versões em inglês foram comparadas para ajustar a versão em português que passou a ser denominada Escala de Acurácia de Diagnóstico de Enfermagem de Lunney - EADE. Quatro enfermeiras foram orientadas sobre a EADE e a aplicaram em 159 diagnósticos formulados para 26 pacientes de três estudos primários com base nos registros de entrevista e exame físico de cada paciente. Os índices Kappa de Cohen mostraram ausência de concordância entre as avaliadoras, o que indica que o instrumento adaptado não tem confiabilidade satisfatória. Em virtude desse resultado, não foi realizada estimativa de validade.

Nursing diagnosis sedentary lifestyle in individuals with hypertension: an analysis of accuracy

Objective Assessing the accuracy of the defining characteristics (DC) of the nursing diagnosis Sedentary Lifestyle (SL) in people with hypertension. Method A cross-sectional study carried out in a referral center in the outpatient care of people with hypertension and diabetes, with a sample of 285 individuals. The form used in the study was designed from operational definitions constructed for each DC of the diagnosis. Four nurses with training to carry out diagnostic inferences did the clinical assessment for the presence of SL. Results The prevalence of SL was 55.8%. Regarding measures of accuracy, the main DC for SL was chooses a daily routine lacking physical exercise, with sensitivity of 100% and specificity of 84.13%. Two DC stood out in the logistic regression, namely: reports preference for activities low in physical activity and poor performance in instrumental activities of daily living (IADL). Conclusion The results allowed identifying the best clinical indicators for SL in hypertensive adults.

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.

Computer-assisted cartography using topographic properties: precision and accuracy of local soil maps in central Mexico

Map units directly related to properties of soil-landscape are generated by local soil classes. Therefore to take into consideration the knowledge of farmers is essential to automate the procedure. The aim of this study was to map local soil classes by computer-assisted cartography (CAC), using several combinations of topographic properties produced by GIS (digital elevation model, aspect, slope, and profile curvature). A decision tree was used to find the number of topographic properties required for digital cartography of the local soil classes. The maps produced were evaluated based on the attributes of map quality defined as precision and accuracy of the CAC-based maps. The evaluation was carried out in Central Mexico using three maps of local soil classes with contrasting landscape and climatic conditions (desert, temperate, and tropical). In the three areas the precision (56 %) of the CAC maps based on elevation as topographical feature was higher than when based on slope, aspect and profile curvature. The accuracy of the maps (boundary locations) was however low (33 %), in other words, further research is required to improve this indicator.

Implications of the variability in soil penetration resistance for statistical analysis

Soil penetration resistance (PR) is a measure of soil compaction closely related to soil structure and plant growth. However, the variability in PR hampers the statistical analyses. This study aimed to evaluate the variability of soil PR on the efficiency of parametric and nonparametric analyses in indentifying significant effects of soil compaction and to classify the coefficient of variation of PR into low, medium, high and very high. On six dates, the PR of a typical dystrophic Red Ultisol under continuous no-tillage for 16 years was measured. Three tillage and/or traffic conditions were established with the application of: (i) no chiseling or additional traffic, (ii) additional compaction, and (iii) chiseling. On each date, the nineteen PR data (measured at every 1.5 cm to a depth of 28.5 cm) were grouped in layers with different thickness. In each layer, the treatment effects were evaluated by variance (ANOVA) and Kruskal-Wallis analyses in a completely randomized design, and the coefficients of variation of all analyses were classified (low, intermediate, high and very high). The ANOVA performed better in discriminating the compaction effects, but the rejection rate of null hypothesis decreased from 100 to 80 % when the coefficient of variation increased from 15 to 26 %. The values of 15 and 26 % were the thresholds separating the low/intermediate and the high/very high coefficient variation classes of PR in this Ultisol.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Estimation of soil moisture in the root-zone from remote sensing data

Field-based soil moisture measurements are cumbersome. Thus, remote sensing techniques are needed because allows field and landscape-scale mapping of soil moisture depth-averaged through the root zone of existing vegetation. The objective of the study was to evaluate the accuracy of an empirical relationship to calculate soil moisture from remote sensing data of irrigated soils of the Apodi Plateau, in the Brazilian semiarid region. The empirical relationship had previously been tested for irrigated soils in Mexico, Egypt, and Pakistan, with promising results. In this study, the relationship was evaluated from experimental data collected from a cotton field. The experiment was carried out in an area of 5 ha with irrigated cotton. The energy balance and evaporative fraction (Λ) were measured by the Bowen ratio method. Soil moisture (θ) data were collected using a PR2 - Profile Probe (Delta-T Devices Ltd). The empirical relationship was tested using experimentally collected Λ and θ values and was applied using the Λ values obtained from the Surface Energy Balance Algorithm for Land (SEBAL) and three TM - Landsat 5 images. There was a close correlation between measured and estimated θ values (p<0.05, R² = 0.84) and there were no significant differences according to the Student t-test (p<0.01). The statistical analyses showed that the empirical relationship can be applied to estimate the root-zone soil moisture of irrigated soils, i.e. when the evaporative fraction is greater than 0.45.

Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index (NDVI), and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

Critical values of nitrogen indices in tomato plants grown in soil and nutrient solution determined by different statistical procedures

The objective of this study was to establish critical values of the N indices, namely soil-plant analysis development (SPAD), petiole sap N-NO3 and organic N in the tomato leaf adjacent to the first cluster (LAC), under soil and nutrient solution conditions, determined by different statistical approaches. Two experiments were conducted in randomized complete block design with four repli-cations. Tomato plants were grown in soil, in 3 L pot, with five N rates (0, 100, 200, 400 and 800 mg kg-1) and in solution at N rates of 0, 4, 8, 12 and 16 mmol L-1. Experiments in nutrient solution and soil were finished at thirty seven and forty two days after transplanting, respectively. At those times, SPAD index and petiole sap N-NO3 were evaluated in the LAC. Then, plants were harvested, separated in leaves and stem, dried at 70ºC, ground and weighted. The organic N was determined in LAC dry matter. Three statistical procedures were used to calculate critical N values. There were accentuated discrepancies for critical values of N indices obtained with plants grown in soil and nutrient solution as well as for different statistical procedures. Critical values of nitrogen indices at all situations are presented.

Spatial statistical analysis and selection of genotypes in plant breeding

The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.

Local behavior and lymph node metastases of Wilms' tumor: accuracy of computed tomography

Objective To evaluate the accuracy of computed tomography for local and lymph node staging of Wilms' tumor. Materials and Methods Each case of Wilms' tumor was evaluated for the presence of abdominal lymph nodes by a radiologist. Signs of capsule and adjacent organ invasion were analyzed. Surgical and histopathological results were taken as the gold standard. Results Sensitivity was 100% for both mesenteric and retroperitoneal lymph nodes detection, and specificity was, respectively, 12% and 33%, with positive predictive value of 8% and 11% and negative predictive value of 100%. Signs of capsular invasion presented sensitivity of 87%, specificity of 77%, positive predictive value of 63% and negative predictive value of 93%. Signs of adjacent organ invasion presented sensitivity of 100%, specificity of 78%, positive predictive value of 37% and negative predictive value of 100%. Conclusion Computed tomography tumor showed low specificity and low positive predictive value in the detection of lymph node dissemination. The absence of detectable lymph nodes makes their presence unlikely, and likewise regarding the evaluation of local behavior of tumors.

Accuracy of computer-aided ultrasound as compared with magnetic resonance imaging in the evaluation of nonalcoholic fatty liver disease in obese and eutrophic adolescents

AbstractObjective:To compare the accuracy of computer-aided ultrasound (US) and magnetic resonance imaging (MRI) by means of hepatorenal gradient analysis in the evaluation of nonalcoholic fatty liver disease (NAFLD) in adolescents.Materials and Methods:This prospective, cross-sectional study evaluated 50 adolescents (aged 11–17 years), including 24 obese and 26 eutrophic individuals. All adolescents underwent computer-aided US, MRI, laboratory tests, and anthropometric evaluation. Sensitivity, specificity, positive and negative predictive values and accuracy were evaluated for both imaging methods, with subsequent generation of the receiver operating characteristic (ROC) curve and calculation of the area under the ROC curve to determine the most appropriate cutoff point for the hepatorenal gradient in order to predict the degree of steatosis, utilizing MRI results as the gold-standard.Results:The obese group included 29.2% girls and 70.8% boys, and the eutrophic group, 69.2% girls and 30.8% boys. The prevalence of NAFLD corresponded to 19.2% for the eutrophic group and 83% for the obese group. The ROC curve generated for the hepatorenal gradient with a cutoff point of 13 presented 100% sensitivity and 100% specificity. As the same cutoff point was considered for the eutrophic group, false-positive results were observed in 9.5% of cases (90.5% specificity) and false-negative results in 0% (100% sensitivity).Conclusion:Computer-aided US with hepatorenal gradient calculation is a simple and noninvasive technique for semiquantitative evaluation of hepatic echogenicity and could be useful in the follow-up of adolescents with NAFLD, population screening for this disease as well as for clinical studies.

Solitary pulmonary nodule and 18F-FDG PET/CT. Part 2: accuracy, cost-effectiveness, and current recommendations

Abstract A solitary pulmonary nodule is a common, often incidental, radiographic finding. The investigation and differential diagnosis of solitary pulmonary nodules remain complex, because there are overlaps between the characteristics of benign and malignant processes. There are currently many strategies for evaluating solitary pulmonary nodules. The main objective is to identify benign lesions, in order to avoid exposing patients to the risks of invasive methods, and to detect cases of lung cancer accurately, in order to avoid delaying potentially curative treatment. The focus of this study was to review the evaluation of solitary pulmonary nodules, to discuss the current role of 18F-fluorodeoxyglucose positron-emission tomography, addressing its accuracy and cost-effectiveness, and to detail the current recommendations for the examination in this scenario.

Development and validation of an alternative titration method for the determination of sulfate ion in indinavir sulfate

A simple and rapid precipitation titration method was developed and validated to determine sulfate ion content in indinavir sulfate raw material. 0.1 mol L-1 lead nitrate volumetric solution was used as titrant employing potentiometric endpoint determination using a lead-specific electrode. The United States Pharmacopoeia Forum indicates a potentiometric method for sulfate ion quantitation using 0.1 mol L-1 lead perchlorate as titrant. Both methods were validated concerning linearity, precision and accuracy, yielding good results. The sulfate ion content found by the two validated methods was compared by the statistical t-student test, indicating that there was no statistically significant difference between the methods.