Scientific Literature on the Reliability and Validity of the Manchester Triage System (MTS) Protocol: A Integrative Literature Review

OBJECTIVE To analyze the scientific production about the validity and reliability of the Manchester Triage System (MTS) protocol. METHOD A descriptive study of an integrative literature review. Articles on the validity and reliability of the MTS developed with children and adults published between 1999 and 2013 were included. RESULTS 14 articles were selected from a total of 8438, nine of validity and five of reliability. The reliability of the MTS ranged from moderate to almost perfect, with higher intra-evaluation. Regarding validity, the results seem to point to equivalent and satisfactory sensibility and specificity levels of the MTS. The instrument proved to be a good predictor of the need for hospitalization and of hospital mortality. CONCLUSION The reliability and validity of the MTS obtained in the studies is varied. It is recommended that new studies indicate necessary modifications to the MTS so that it is more safely used by nurses.

Interrater reliability of Nursing Activities Score among Intensive Care Unit health professionals

ABSTRACT Objective To analyze the interrater reliability of NAS among critical care nurses and managers in an ICU. Method This was a methodological study performed in an adult, general ICU in Norway. In a random selection of patients, the NAS was scored on 101 patients by three raters: a critical care nurse, an ICU physician and a nurse manager. Interrater reliability was analyzed by agreement between groups and kappa statistics. Results The mean NAS were 88.4 (SD=16.2) and 88.7 (SD=24.5) respectively for the critical care nurses and nurse managers. A lower mean of 83.7 (SD=21.1) was found for physicians. The 18 medical interventions showed higher agreement between critical care nurses and physicians (85.6%), than between critical care nurses and nurse managers (78.7). In the five nursing activities the Kappa-coefficients were low for all activities in all compared groups. Conclusion The study indicated a satisfactory agreement of nursing workload between critical care nurses and managers.

Runoff measurement and prediction for a watershed under natural vegetation in central Brazil

This work aimed to measure and analyze total rainfall (P), rainfall intensity and five-day antecedent rainfall effects on runoff (R); to compare measured and simulated R values using the Soil Conservation Service Curve Number method (CN) for each rainfall event; and to establish average R/P ratios for observed R values. A one-year (07/01/96 to 06/30/97) rainfall-runoff data study was carried out in the Capetinga watershed (962.4 ha), located at the Federal District of Brazil, 47° 52' longitude West and 15° 52' latitude South. Soils of the watershed were predominantly covered by natural vegetation. Total rainfall and runoff for the period were 1,744 and 52.5 mm, respectively, providing R/P of 3% and suggesting that watershed physical characteristics favored water infiltration into the soil. A multivariate regression analysis for 31 main rainfall-runoff events totaling 781.9 and 51.0 mm, respectively, indicated that the amount of runoff was only dependent upon rainfall volume. Simulated values of total runoff were underestimated about 15% when using CN method and an area-weighted average of the CN based on published values. On the other hand, when average values of CN were calculated for the watershed, total runoff was overestimated about 39%, suggesting that CN method shoud be used with care in areas under natural vegetation.

Reconsolidation of the soil surface after tillage discontinuity, with and without cultivation, related to erosion and its prediction with RUSLE

Site-specific regression coefficient values are essential for erosion prediction with empirical models. With the objective to investigate the surface-soilconsolidation factor, Cf, linked to the RUSLE's prior-land-use subfactor, PLU, an erosion experiment using simulated rainfall on a 0.075 m m-1 slope, sandy loam Paleudult soil, was conducted at the Agriculture Experimental Station of the Federal University of Rio Grande do Sul (EEA/UFRGS), in Eldorado do Sul, State of Rio Grande do Sul, Brazil. Firstly, a row-cropped area was excluded from cultivation (March 1995), the existing crop residue removed from the field, and the soil kept clean-tilled the rest of the year (to get a degraded soil condition for the intended purpose of this research). The soil was then conventional-tilled for the last time (except for a standard plot which was kept continuously cleantilled for comparison purposes), in January 1996, and the following treatments were established and evaluated for soil reconsolidation and soil erosion until May 1998, on duplicated 3.5 x 11.0 m erosion plots: (a) fresh-tilled soil, continuously in clean-tilled fallow (unit plot); (b) reconsolidating soil without cultivation; and (c) reconsolidating soil with cultivation (a crop sequence of three corn- and two black oats cycles, continuously in no-till, removing the crop residues after each harvest for rainfall application and redistributing them on the site after that). Simulated rainfall was applied with a Swanson's type, rotating-boom rainfall simulator, at 63.5 mm h-1 intensity and 90 min duration, six times during the two-and-half years of experimental period (at the beginning of the study and after each crop harvest, with the soil in the unit plot being retilled before each rainfall test). The soil-surface-consolidation factor, Cf, was calculated by dividing soil loss values from the reconsolidating soil treatments by the average value from the fresh-tilled soil treatment (unit plot). Non-linear regression was used to fit the Cf = e b.t model through the calculated Cf-data, where t is time in days since last tillage. Values for b were -0.0020 for the reconsolidating soil without cultivation and -0.0031 for the one with cultivation, yielding Cf-values equal to 0.16 and 0.06, respectively, after two-and-half years of tillage discontinuation, compared to 1.0 for fresh-tilled soil. These estimated Cf-values correspond, respectively, to soil loss reductions of 84 and 94 %, in relation to soil loss from the fresh-tilled soil, showing that the soil surface reconsolidated intenser with cultivation than without it. Two distinct treatmentinherent soil surface conditions probably influenced the rapid decay-rate of Cf values in this study, but, as a matter of a fact, they were part of the real environmental field conditions. Cf-factor curves presented in this paper are therefore useful for predicting erosion with RUSLE, but their application is restricted to situations where both soil type and particular soil surface condition are similar to the ones investigate in this study.

Surface and subsurface decomposition of a desiccated grass pasture biomass related to erosion and its prediction with RUSLE

Erosion is deleterious because it reduces the soil's productivity capacity for growing crops and causes sedimentation and water pollution problems. Surface and buried crop residue, as well as live and dead plant roots, play an important role in erosion control. An efficient way to assess the effectiveness of such materials in erosion reduction is by means of decomposition constants as used within the Revised Universal Soil Loss Equation - RUSLE's prior-land-use subfactor - PLU. This was investigated using simulated rainfall on a 0.12 m m-1 slope, sandy loam Paleudult soil, at the Agriculture Experimental Station of the Federal University of Rio Grande do Sul, in Eldorado do Sul, State of Rio Grande do Sul, Brazil. The study area had been covered by native grass pasture for about fifteen years. By the middle of March 1996, the sod was mechanically mowed and the crop residue removed from the field. Late in April 1996, the sod was chemically desiccated with herbicide and, about one month later, the following treatments were established and evaluated for sod biomass decomposition and soil erosion, from June 1996 to May 1998, on duplicated 3.5 x 11.0 m erosion plots: (a) and (b) soil without tillage, with surface residue and dead roots; (c) soil without tillage, with dead roots only; (d) soil tilled conventionally every two-and-half months, with dead roots plus incorporated residue; and (e) soil tilled conventionally every six months, with dead roots plus incorporated residue. Simulated rainfall was applied with a rotating-boom rainfall simulator, at an intensity of 63.5 mm h-1 for 90 min, eight to nine times during the experimental period (about every two-and-half months). Surface and subsurface sod biomass amounts were measured before each rainfall test along with the erosion measurements of runoff rate, sediment concentration in runoff, soil loss rate, and total soil loss. Non-linear regression analysis was performed using an exponential and a power model. Surface sod biomass decomposition was better depicted by the exponential model, while subsurface sod biomass was by the power model. Subsurface sod biomass decomposed faster and more than surface sod biomass, with dead roots in untilled soil without residue on the surface decomposing more than dead roots in untilled soil with surface residue. Tillage type and frequency did not appreciably influence subsurface sod biomass decomposition. Soil loss rates increased greatly with both surface sod biomass decomposition and decomposition of subsurface sod biomass in the conventionally tilled soil, but they were minimally affected by subsurface sod biomass decomposition in the untilled soil. Runoff rates were little affected by the studied treatments. Dead roots plus incorporated residues were effective in reducing erosion in the conventionally tilled soil, while consolidation of the soil surface was important in no-till. The residual effect of the turned soil on erosion diminished gradually with time and ceased after two years.

Uncertainties in the prediction of spatial variability of soil CO2 emissions and related properties

The soil CO2 emission has high spatial variability because it depends strongly on soil properties. The purpose of this study was to (i) characterize the spatial variability of soil respiration and related properties, (ii) evaluate the accuracy of results of the ordinary kriging method and sequential Gaussian simulation, and (iii) evaluate the uncertainty in predicting the spatial variability of soil CO2 emission and other properties using sequential Gaussian simulations. The study was conducted in a sugarcane area, using a regular sampling grid with 141 points, where soil CO2 emission, soil temperature, air-filled pore space, soil organic matter and soil bulk density were evaluated. All variables showed spatial dependence structure. The soil CO2 emission was positively correlated with organic matter (r = 0.25, p < 0.05) and air-filled pore space (r = 0.27, p < 0.01) and negatively with soil bulk density (r = -0.41, p < 0.01). However, when the estimated spatial values were considered, the air-filled pore space was the variable mainly responsible for the spatial characteristics of soil respiration, with a correlation of 0.26 (p < 0.01). For all variables, individual simulations represented the cumulative distribution functions and variograms better than ordinary kriging and E-type estimates. The greatest uncertainties in predicting soil CO2 emission were associated with areas with the highest estimated values, which produced estimates from 0.18 to 1.85 t CO2 ha-1, according to the different scenarios considered. The knowledge of the uncertainties generated by the different scenarios can be used in inventories of greenhouse gases, to provide conservative estimates of the potential emission of these gases.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Surface Complexation Modeling in Variable Charge Soils: Prediction of Cadmium Adsorption

ABSTRACT Intrinsic equilibrium constants for 22 representative Brazilian Oxisols were estimated from a cadmium adsorption experiment. Equilibrium constants were fitted to two surface complexation models: diffuse layer and constant capacitance. Intrinsic equilibrium constants were optimized by FITEQL and by hand calculation using Visual MINTEQ in sweep mode, and Excel spreadsheets. Data from both models were incorporated into Visual MINTEQ. Constants estimated by FITEQL and incorporated in Visual MINTEQ software failed to predict observed data accurately. However, FITEQL raw output data rendered good results when predicted values were directly compared with observed values, instead of incorporating the estimated constants into Visual MINTEQ. Intrinsic equilibrium constants optimized by hand calculation and incorporated in Visual MINTEQ reliably predicted Cd adsorption reactions on soil surfaces under changing environmental conditions.

Prediction of cottonseed longevity

The objective of this work was to determine the viability equation constants for cottonseed and to detect the occurrence and depletion of hardseededness. Three seedlots of Brazilian cultivars IAC-19 and IAC-20 were tested, using 12 moisture content levels, ranging from 2.2 to 21.7% and three storage temperatures, 40, 50 and 65ºC. Seed moisture content level was reached from the initial value (around 8.8%) either by rehydration, in a closed container, or by drying in desiccators containing silica gel, both at 20ºC. Twelve seed subsamples for each moisture content/temperature treatment were sealed in laminated aluminium-foil packets and stored in incubators at those temperatures, until complete survival curves were obtained. Seed equilibrium relative humidity was recorded. Hardseededness was detected at moisture content levels below 6% and its releasing was achieved either naturally, during storage period, or artificially through seed coat removal. The viability equation quantified the response of seed longevity to storage environment well with K E = 9.240, C W = 5.190, C H = 0.03965 and C Q = 0.000426. The lower limit estimated for application of this equation at 65ºC was 3.6% moisture content.

Prediction of direct and indirect genetic gains and genotypic correlations in rubber tree progenies

The objective of this work was to estimate the genetic parameters, genotypic and phenotypic correlations, and direct and indirect genetic gains among and within rubber tree (Hevea brasiliensis) progenies. The experiment was set up at the Municipality of Jaú, SP, Brazil. A randomized complete block design was used, with 22 treatments (progenies), 6 replicates, and 10 plants per plot at a spacing of 3x3 m. Three‑year‑old progenies were assessed for girth, rubber yield, and bark thickness by direct and indirect gains and genotypic correlations. The number of latex vessel rings showed the best correlations, correlating positively and significantly with girth and bark thickness. Selection gains among progenies were greater than within progeny for all the variables analyzed. Total gains obtained were high, especially for girth increase and rubber yield, which were 93.38 and 105.95%, respectively. Young progeny selection can maximize the expected genetic gains, reducing the rubber tree selection cycle.

Prediction of soil orders with high spatial resolution: response of different classifiers to sampling density

The objective of this work was to evaluate sampling density on the prediction accuracy of soil orders, with high spatial resolution, in a viticultural zone of Serra Gaúcha, Southern Brazil. A digital elevation model (DEM), a cartographic base, a conventional soil map, and the Idrisi software were used. Seven predictor variables were calculated and read along with soil classes in randomly distributed points, with sampling densities of 0.5, 1, 1.5, 2, and 4 points per hectare. Data were used to train a decision tree (Gini) and three artificial neural networks: adaptive resonance theory, fuzzy ARTMap; self‑organizing map, SOM; and multi‑layer perceptron, MLP. Estimated maps were compared with the conventional soil map to calculate omission and commission errors, overall accuracy, and quantity and allocation disagreement. The decision tree was less sensitive to sampling density and had the highest accuracy and consistence. The SOM was the less sensitive and most consistent network. The MLP had a critical minimum and showed high inconsistency, whereas fuzzy ARTMap was more sensitive and less accurate. Results indicate that sampling densities used in conventional soil surveys can serve as a reference to predict soil orders in Serra Gaúcha.

Field data and prediction models of pesticide spray drift on coffee crop

The objective of this work was to generate drift curves from pesticide applications on coffee plants and to compare them with two European drift-prediction models. The used methodology is based on the ISO 22866 standard. The experimental design was a randomized complete block with ten replicates in a 2x20 split-plot arrangement. The evaluated factors were: two types of nozzles (hollow cone with and without air induction) and 20 parallel distances to the crop line outside of the target area, spaced at 2.5 m. Blotting papers were used as a target and placed in each of the evaluated distances. The spray solution was composed of water+rhodamine B fluorescent tracer at a concentration of 100 mg L-1, for detection by fluorimetry. A spray volume of 400 L ha-1 was applied using a hydropneumatic sprayer. The air-induction nozzle reduces the drift up to 20 m from the treated area. The application with the hollow cone nozzle results in 6.68% maximum drift in the nearest collector of the treated area. The German and Dutch models overestimate the drift at distances closest to the crop, although the Dutch model more closely approximates the drift curves generated by both spray nozzles.

Prediction of soil shear strength in agricultural and natural environments of the Brazilian Cerrado

The objective of this work was to develop uni- and multivariate models to predict maximum soil shear strength (τmax) under different normal stresses (σn), water contents (U), and soil managements. The study was carried out in a Rhodic Haplustox under Cerrado (control area) and under no-tillage and conventional tillage systems. Undisturbed soil samples were taken in the 0.00-0.05 m layer and subjected to increasing U and σn, in shear strength tests. The uni- and multivariate models - respectively τmax=10(a+bU) and τmax=10(a+bU+cσn) - were significant in all three soil management systems evaluated and they satisfactorily explain the relationship between U, σn, and τmax. The soil under Cerrado has the highest shear strength (τ) estimated with the univariate model, regardless of the soil water content, whereas the soil under conventional tillage shows the highest values with the multivariate model, which were associated to the lowest water contents at the soil consistency limits in this management system.

Prediction of preeclampsia by means of Doppler flowmetry of uterine artery and flow-mediated dilation of brachial artery

Objective To evaluate the association of Doppler of uterine artery and flow-mediated dilation of brachial artery (FMD) in the assessment of placental perfusion and endothelial function to predict preeclampsia. Materials and Methods A total of 91 patients considered as at risk for developing preeclampsia were recruited at the prenatal unit of the authors' institution. All the patients underwent FMD and Doppler of uterine arteries between their 24th and 28th gestational weeks. Calculations of sensitivity and specificity for both isolated and associated methods were performed. Results Nineteen out of the 91 patients developed preeclampsia, while the rest remained normotensive. Doppler flowmetry of uterine arteries with presence of bilateral protodiastolic notch had sensitivity of 63.1% and specificity of 87.5% for the prediction of preeclampsia. Considering a cutoff value of 6.5%, FMD showed sensitivity of 84.2% and specificity of 73.6%. In a parallel analysis, as the two methods were associated, sensitivity was 94.2% and specificity, 64.4%. Conclusion The association of Doppler study of uterine arteries and FMD has proved to be an interesting clinical strategy for the prediction of preeclampsia, which may represent a positive impact on prenatal care of patients considered as at high-risk for developing such a condition.