Acute appendicitis: prospective evaluation of a diagnostic algorithm integrating ultrasound and low-dose CT to reduce the need of standard CT

Objectives: To evaluate an algorithm integrating ultrasound and low-dose unenhanced CT with oral contrast medium (LDCT) in the assessment of acute appendicitis, to reduce the need of conventional CT. Methods: Ultrasound was performed upon admission in 183 consecutive adult patients (111 women, 72 men, mean age 32) with suspicion of acute appendicitis and a BMI between 18.5 and 30 (step 1). No further examination was recommended when ultrasound was positive for appendicitis, negative with low clinical suspicion, or demonstrated an alternative diagnosis. All other patients underwent LDCT (30mAs) (step 2). Standard intravenously enhanced CT (180mAs) was performed after indeterminate LDCT (step 3). Results: No further imaging was recommended after ultrasound in 84 (46%) patients; LDCT was obtained in 99 (54%). LDCT was positive or negative for appendicitis in 81 (82%) of these 99 patients, indeterminate in 18 (18%) who underwent standard CT. Eighty-six (47%) of the 183 patients had a surgically proven appendicitis. The sensitivity and specificity of the algorithm were 98.8% and 96.9%. Conclusions: The proposed algorithm achieved high sensitivity and specificity for detection of acute appendicitis, while reducing the need for standard CT and thus limiting exposition to radiation and to intravenous contrast medi

Simulation based bias correction methods for complex problems

Nowadays, the increase in data size and model complexity has led to increasingly difficult estimation problems. The numerical aspects of the estimation procedure can indeed be very challenging. To solve these estimation problems, approximate methods such as pseudo-likelihood functions or approximated estimating equations can be used as these methods are typically easier to implement numerically although they can lead to inconsistent and/or biased estimators. In this thesis, we propose a unified framework to compare four existing bias reduction estimators, two of them are based on indirect inference and two are based of bootstrap. We derive the asymptotic and finite sample properties of these bias correction methods. We demonstrate the equivalence between one version of the indirect inference and the iterative bootstrap which both correct sample biases up to the order $n^{-3}$. Therefore, our results provide different tools to correct the asymptotic as well as finite sample biases of estimators and give insight as to which method should be applied according to the problem at hand. We then apply these bias reduction techniques to robust estimation of income distributions. We used a very simple starting estimator which is known to be robust but not consistent and correct its bias with indirect inference. This is a very general way to construct robust estimators for complex models. A second illustration is provided by the estimation of Generalized Linear Latent Variable Models. We were able to compute unbiased estimates for these very complex models that have a large number of parameters without employing numerical integration techniques. As a by-product, bias reduction techniques allow to compute a goodness-of-fit test statistic for latent variable models

Robust estimation of constrained covariance matrices for Confirmatory Factor Analysis

Confirmatory factor analysis (CFA) is a data analysis procedure that is widely used in social and behavioral sciences in general and other applied sciences that deal with large quantities of data (variables). The underlying model links a set latent factors, that are supposed to correspond to latent concepts, to a larger set of observed (manifest) variables through linear regression equations. With CFA, it is not necessary that all manifest variables are linked to all latent factors, and is particularly useful for the construction of so-called measurement scales like depression scales in psychology. The classical estimator (and inference) procedures are based either on the maximum likelihood (ML) or generalized least squares (GLS) approaches. Unfortunately these methods are known to be non robust to model misspecification, which in the case of factor analysis in general, and in CFA in particular, is misspecification with respect to the multivariate normal model. A natural robust estimator is obtained by first estimating the (mean and) covariance matrix of the manifest variables and then "plug-in" this statistic into the ML or GLS estimating equations. This two-stage method however doesn't fully take into account the covariance structure implied by the CFA model. In this paper, we propose an S-estimator for the parameters of the CFA model that is computed directly from the data. We derive the estimating equations and an iterative procedure. The two estimators have different asymptotic properties, in that their asymptotic covariance matrix is not the same, and they both depend on the model and the parameters values. We perform a simulation study to compare the finite sample properties of both estimators and find that the direct estimator we propose is more stable (smaller MSE) than the two-stage estimator

Appraisal of Musical Syntax Violations by Primary School Children. Effects of Age and Practice

In Western tonal music, musical phrases end with an explicit, highly expected, harmonic consequent. Primary school children were exposed to musical stimuli at two levels of complexity: children’s songs and polyphonic piano pieces. The endings (cadences) of all stimuli were either congruous or contained subtle or marked syntax violations, resulting in three levels of syntactic congruity. The children rated the endings of musical stimuli with respect to goodness of fit by drawing a crossbar through a continuous line stretching between a happy and a sad icon. All children, independent of age, rated the three levels of syntactic congruity hierarchically, for both levels of complexity. Compared to younger children, older children gave more extreme positive and negative ratings to congruous and markedly incongruous endings, respectively, but no developmental trend was found for the intermediate ratings of subtly incongruous endings. We conclude that, as a consequence of mere exposure, implicit learning of musical syntax manifests already in 6-year-old children and develops progressively with age. Moreover, we found indications of modulation of this perceptual development by musical training, an effect reminiscent of the interaction between long-term spontaneous developmental processes and deliberate learning in general cognitive functioning. </jats:p

Ventilation Parameters Before Extracorporeal Membrane Oxygenator and In-Hospital Mortality in Children: A Review of the ELSO Registry

The aim of this study was to evaluate the impact of pre-extracorporeal membrane oxygenation (ECMO) ventilatory parameters with in-hospital mortality in children with pediatric acute respiratory distress syndrome undergoing ECMO for respiratory indication. In this retrospective analysis of the Extracorporeal Life Support Organization (ELSO) Registry, all pediatric patients (≥29 days to ≤18 years) who required ECMO for respiratory indications were screened. The primary outcome was in-hospital mortality. From 2013 to 2017, 2,727 pediatric ECMO runs with a respiratory indication were reported to the ELSO registry. Overall mortality was 37%. Oxygenation Index (OI) and duration of mechanical ventilation (MV) before ECMO deployment were both independently associated with in-hospital mortality. No threshold effect for OI was observed. Pre-ECMO positive end-expiratory pressure and delta pressure levels were respectively lower and higher than recommended. Mortality rates for OI values between 4 and 60 and above oscillated between 32% and 45%. Children within a wider range of pre-ECMO OI (either below or above 40) might be considered as reasonable candidates for ECMO deployment. Larger, prospective multicenter studies to confirm the discriminatory ability of OI are warranted

Simulation based bias correction methods for complex models

Along the ever increasing data size and model complexity, an important challenge frequently encountered in constructing new estimators or in implementing a classical one such as the maximum likelihood estimator, is the computational aspect of the estimation procedure. To carry out estimation, approximate methods such as pseudo-likelihood functions or approximated estimating equations are increasingly used in practice as these methods are typically easier to implement numerically although they can lead to inconsistent and/or biased estimators. In this context, we extend and provide refinements on the known bias correction properties of two simulation based methods, respectively indirect inference and bootstrap, each with two alternatives. These results allow one to build a framework defining simulation based estimators that can be implemented for complex models. Indeed, based on a biased or even inconsistent estimator, several simulation based methods can be used to define new estimators that are both consistent and with reduced finite sample bias. This framework includes the classical method of indirect inference for bias correction without requiring specification of an auxiliary model. We demonstrate the equivalence between one version of the indirect inference and the iterative bootstrap, both correct sample biases up to the order n^{-3}. The iterative method can be thought of as a computationally efficient algorithm to solve the optimization problem of the indirect inference. Our results provide different tools to correct the asymptotic as well as finite sample biases of estimators and give insight on which method should be applied for the problem at hand. The usefulness of the proposed approach is illustrated with the estimation of robust income distributions and generalized linear latent variable models

Phonological Specificity in 12- and 17-Month-Old French-Speaking Infants

The literature reports some contradictory results on the degree of phonological specificity of infants' early lexical representations in the Romance language, French, and Germanic languages. It is not clear whether these discrepancies are because of differences in method, in language characteristics, or in participants'age. In this study, we examined whether 12- and 17-month-old Frenchspeaking infants are able to distinguish well-pronounced from mispronounced words (one or two features of their initial consonant). To this end, 46 infants participated in a preferential looking experiment in which they were presented with pairs of pictures together with a spoken word well pronounced or mispronounced. The results show that both 12- and 17-month-old infants look longer at the pictures corresponding to well-pronounced words than to mispronounced words, but show no difference between the two mispronunciation types. These results suggest that, as early as 12 months, French-speaking infants, like those exposed to Germanic languages, already possess detailed phonological representations of familiar words