Comparação entre a adiposidade subcutânea dos hemicorpos direito e esquerdo

O objetivo deste estudo foi comparar os níveis de adiposidade subcutânea dos hemicorpos direito e esquerdo e, posteriormente, analisar o impacto dessas informações para o estudo da composição corporal. Setenta e seis indivíduos fisicamente ativos, 47 homens (21,6 ± 4,3 anos) e 29 mulheres (21,0 ± 2,6 anos), fizeram parte da amostra. As espessuras das dobras cutâneas abdominal, suprailíaca, subescapular, tricipital, bicipital, axilar média e perna medial foram mensuradas com um compasso Lange. Em valores médios absolutos, as maiores diferenças verificadas foram de 0,9mm (6,9%) e 0,8mm (6,8%), na dobra cutânea suprailíaca de homens e mulheres, respectivamente. Entretanto, nenhuma diferença estatisticamente significante foi encontrada na comparação entre os lados, em ambos os sexos, nas sete dobras cutâneas analisadas (P > 0,05). Similarmente, quando os valores medidos foram aplicados em equações preditivas para a determinação da gordura corporal relativa, de acordo com o sexo, nenhuma diferença significante foi encontrada (P > 0,05). Os resultados sugerem que fatores como o erro técnico de medida do avaliador, o tipo de compasso e a escolha da equação preditiva a ser utilizada, provavelmente tenham maior impacto para a estimativa da composição corporal pelo método de espessura de dobras cutâneas do que o lado a ser adotado como referência para a obtenção das medidas.

Variability in Cobb angle measurements using reformatted computerized tomography scans

Study Design. Survey of intraobserver and interobserver measurement variability. Objective. To assess the use of reformatted computerized tomography (CT) images for manual measurement of coronal Cobb angles in idiopathic scoliosis. Summary of Background Data. Cobb angle measurements in idiopathic scoliosis are traditionally made from standing radiographs, whereas CT is often used for assessment of vertebral rotation. Correlating Cobb angles from standing radiographs with vertebral rotations from supine CT is problematic because the geometry of the spine changes significantly from standing to supine positions, and 2 different imaging methods are involved. Methods. We assessed the use of reformatted thoracolumbar CT images for Cobb angle measurement. Preoperative CT of 12 patients with idiopathic scoliosis were used to generate reformatted coronal images. Five observers measured coronal Cobb angles on 3 occasions from each of the images. Intraobserver and interobserver variability associated with Cobb measurement from reformatted CT scans was assessed and compared with previous studies of measurement variability using plain radiographs. Results. For major curves, 95% confidence intervals for intraobserver and interobserver variability were +/- 6.6 degrees and +/- 7.7 degrees, respectively. For minor curves, the intervals were +/- 7.5 degrees and +/- 8.2 degrees, respectively. Intraobserver and interobserver technical error of measurement was 2.4 degrees and 2.7 degrees, with reliability coefficients of 88% and 84%, respectively. There was no correlation between measurement variability and curve severity. Conclusions. Reformatted CT images may be used for manual measurement of coronal Cobb angles in idiopathic scoliosis with similar variability to manual measurement of plain radiographs.

Model Diagnostics in the presence of measurement error

The problem of using information available from one variable X to make inferenceabout another Y is classical in many physical and social sciences. In statistics this isoften done via regression analysis where mean response is used to model the data. Onestipulates the model Y = µ(X) +ɛ. Here µ(X) is the mean response at the predictor variable value X = x, and ɛ = Y - µ(X) is the error. In classical regression analysis, both (X; Y ) are observable and one then proceeds to make inference about the mean response function µ(X). In practice there are numerous examples where X is not available, but a variable Z is observed which provides an estimate of X. As an example, consider the herbicidestudy of Rudemo, et al. [3] in which a nominal measured amount Z of herbicide was applied to a plant but the actual amount absorbed by the plant X is unobservable. As another example, from Wang [5], an epidemiologist studies the severity of a lung disease, Y , among the residents in a city in relation to the amount of certain air pollutants. The amount of the air pollutants Z can be measured at certain observation stations in the city, but the actual exposure of the residents to the pollutants, X, is unobservable and may vary randomly from the Z-values. In both cases X = Z+error: This is the so called Berkson measurement error model.In more classical measurement error model one observes an unbiased estimator W of X and stipulates the relation W = X + error: An example of this model occurs when assessing effect of nutrition X on a disease. Measuring nutrition intake precisely within 24 hours is almost impossible. There are many similar examples in agricultural or medical studies, see e.g., Carroll, Ruppert and Stefanski [1] and Fuller [2], , among others. In this talk we shall address the question of fitting a parametric model to the re-gression function µ(X) in the Berkson measurement error model: Y = µ(X) + ɛ; X = Z + η; where η and ɛ are random errors with E(ɛ) = 0, X and η are d-dimensional, and Z is the observable d-dimensional r.v.

Estimating correlations from epidemiological data in the presence of measurement error

Analysis of a major multi-site epidemiologic study of heart disease has required estimation of the pairwise correlation of several measurements across sub-populations. Because the measurements from each sub-population were subject to sampling variability, the Pearson product moment estimator of these correlations produces biased estimates. This paper proposes a model that takes into account within and between sub-population variation, provides algorithms for obtaining maximum likelihood estimates of these correlations and discusses several approaches for obtaining interval estimates. (C) 1997 by John Wiley & Sons, Ltd.

The efficacy of the old way/new way methodology on the correction of an automated technical error and its impact on the athlete’s psychological skills : Case study in tennis

The aim of this study was to evaluated the efficacy of the Old Way/New Way methodology (Lyndon, 1989/2000) with regard to the permanent correction of a consolidated and automated technical error experienced by a tennis athlete (who is 18 years old and has been engaged in practice mode for about 6 years) in the execution of serves. Additionally, the study assessed the impact of intervention on the athlete’s psychological skills. An individualized intervention was designed using strategies that aimed to produce a) a detailed analysis of the error using video images; b) an increased kinaesthetic awareness; c) a reactivation of memory error; d) the discrimination and generalization of the correct motor action. The athlete’s psychological skills were measured with a Portuguese version of the Psychological Skills Inventory for Sports (Cruz & Viana, 1993). After the intervention, the technical error was corrected with great efficacy and an increase in the athlete’s psychological skills was verified. This study demonstrates the methodology’s efficacy, which is consistent with the effects of this type of intervention in different contexts.

Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples

In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression

Sub- or intertrochanteric fracture following screw fixation of an intracapsular proximal femoral fracture: true complication or technical error?

PURPOSE: To review, retrospectively, the possible causes of sub- or intertrochanteric fractures after screw fixation of intracapsular fractures of the proximal femur. METHODS: Eighty-four patients with an intracapsular fracture of proximal femur were operated between 1995 and 1998 by using three cannulated 6.25 mm screws. The screws were inserted in a triangular configuration, one screw in the upper part of the femoral neck and two screws in the inferior part. Between 1999 and 2001, we use two screws proximally and one screw distally. RESULTS: In the first series, two patients died within one week after operation. Sixty-four fractures healed without problems. Four patients developed an atrophic non-union; avascular necrosis of the femoral head was found in 11 patients. Three patients (3.6%) suffered a sub- and/or intertrochanteric fracture after a mean postoperative time of 30 days, in one case without obvious trauma. In all three cases surgical revision was necessary. Between 1999 and 2001 we did not observe any fracture after screwing. CONCLUSION: Two screws in the inferior part of the femoral neck create a stress riser in the subtrochanteric region, potentially inducing a fracture in the weakened bone. For internal fixation for proximal intracapsular femoral fracture only one screw must be inserted in the inferior part of neck.

Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples

In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression

Effect of measurement error and autocorrelation on the (X)over-bar chart

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The impact of measurement errors in the identification of regulatory networks

Background: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions: Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

Potential errors in the application of mixture theory to multifrequency bioelectrical impedance analysis

Potential errors in the application of mixture theory to the analysis of multiple-frequency bioelectrical impedance data for the determination of body fluid volumes are assessed. Potential sources of error include: conductive length; tissue fluid resistivity; body density; weight and technical errors of measurement. Inclusion of inaccurate estimates of body density and weight introduce errors of typically < +/-3% but incorrect assumptions regarding conductive length or fluid resistivities may each incur errors of up to 20%.

Image quality assessment in digital mammography: part I. Technical characterization of the systems.

In many European countries, image quality for digital x-ray systems used in screening mammography is currently specified using a threshold-detail detectability method. This is a two-part study that proposes an alternative method based on calculated detectability for a model observer: the first part of the work presents a characterization of the systems. Eleven digital mammography systems were included in the study; four computed radiography (CR) systems, and a group of seven digital radiography (DR) detectors, composed of three amorphous selenium-based detectors, three caesium iodide scintillator systems and a silicon wafer-based photon counting system. The technical parameters assessed included the system response curve, detector uniformity error, pre-sampling modulation transfer function (MTF), normalized noise power spectrum (NNPS) and detective quantum efficiency (DQE). Approximate quantum noise limited exposure range was examined using a separation of noise sources based upon standard deviation. Noise separation showed that electronic noise was the dominant noise at low detector air kerma for three systems; the remaining systems showed quantum noise limited behaviour between 12.5 and 380 µGy. Greater variation in detector MTF was found for the DR group compared to the CR systems; MTF at 5 mm(-1) varied from 0.08 to 0.23 for the CR detectors against a range of 0.16-0.64 for the DR units. The needle CR detector had a higher MTF, lower NNPS and higher DQE at 5 mm(-1) than the powder CR phosphors. DQE at 5 mm(-1) ranged from 0.02 to 0.20 for the CR systems, while DQE at 5 mm(-1) for the DR group ranged from 0.04 to 0.41, indicating higher DQE for the DR detectors and needle CR system than for the powder CR phosphor systems. The technical evaluation section of the study showed that the digital mammography systems were well set up and exhibiting typical performance for the detector technology employed in the respective systems.

The Total Deviation Index estimated by Tolerance Intervals to evaluate the concordance of measurement devices

Background In an agreement assay, it is of interest to evaluate the degree of agreement between the different methods (devices, instruments or observers) used to measure the same characteristic. We propose in this study a technical simplification for inference about the total deviation index (TDI) estimate to assess agreement between two devices of normally-distributed measurements and describe its utility to evaluate inter- and intra-rater agreement if more than one reading per subject is available for each device. Methods We propose to estimate the TDI by constructing a probability interval of the difference in paired measurements between devices, and thereafter, we derive a tolerance interval (TI) procedure as a natural way to make inferences about probability limit estimates. We also describe how the proposed method can be used to compute bounds of the coverage probability. Results The approach is illustrated in a real case example where the agreement between two instruments, a handle mercury sphygmomanometer device and an OMRON 711 automatic device, is assessed in a sample of 384 subjects where measures of systolic blood pressure were taken twice by each device. A simulation study procedure is implemented to evaluate and compare the accuracy of the approach to two already established methods, showing that the TI approximation produces accurate empirical confidence levels which are reasonably close to the nominal confidence level. Conclusions The method proposed is straightforward since the TDI estimate is derived directly from a probability interval of a normally-distributed variable in its original scale, without further transformations. Thereafter, a natural way of making inferences about this estimate is to derive the appropriate TI. Constructions of TI based on normal populations are implemented in most standard statistical packages, thus making it simpler for any practitioner to implement our proposal to assess agreement.