University of Iowa

About: University of Iowa is a education organization based out in Iowa City, Iowa, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 49229 authors who have published 109171 publications receiving 5021465 citations. The organization is also known as: UI & The University of Iowa.

The incidence of awareness during anesthesia: a multicenter United States study.

Abstract: Awareness with recall after general anesthesia is an infrequent, but well described, phenomenon that may result in posttraumatic stress disorder. There are no recent data on the incidence of this complication in the United States. We, therefore, undertook a prospective study to determine the incidence of awareness with recall during general anesthesia in the United States. This is a prospective, nonrandomized descriptive cohort study that was conducted at seven academic medical centers in the United States. Patients scheduled for surgery under general anesthesia were interviewed in the postoperative recovery room and at least a week after anesthesia and surgery by using a structured interview. Data from 19,575 patients are presented. A total of 25 awareness cases were identified (0.13% incidence). These occurred at a rate of 1-2 cases per 1000 patients at each site. Awareness was associated with increased ASA physical status (odds ratio, 2.41; 95% confidence interval, 1.04-5.60 for ASA status III-V compared with ASA status I-II). Age and sex did not influence the incidence of awareness. There were 46 additional cases (0.24%) of possible awareness and 1183 cases (6.04%) of possible intraoperative dreaming. The incidence of awareness during general anesthesia with recall in the United States is comparable to that described in other countries. Assuming that approximately 20 million anesthetics are administered in the United States annually, we can expect approximately 26,000 cases to occur each year.

Fixed Effects vs. Random Effects Meta‐Analysis Models: Implications for Cumulative Research Knowledge

Abstract: Research conclusions in the social sciences are increasingly based on meta-analysis, making questions of the accuracy of meta-analysis critical to the integrity of the base of cumulative knowledge. Both fixed effects (FE) and random effects (RE) meta-analysis models have been used widely in published meta-analyses. This article shows that FE models typically manifest a substantial Type I bias in significance tests for mean effect sizes and for moderator variables (interactions), while RE models do not. Likewise, FE models, but not RE models, yield confidence intervals for mean effect sizes that are narrower than their nominal width, thereby overstating the degree of precision in meta-analysis findings. This article demonstrates analytically that these biases in FE procedures are large enough to create serious distortions in conclusions about cumulative knowledge in the research literature. We therefore recommend that RE methods routinely be employed in meta-analysis in preference to FE methods.

FMRIPrep: a robust preprocessing pipeline for functional MRI

Abstract: Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available for each step. The complexity of these workflows has snowballed with rapid advances in MR data acquisition and image processing techniques. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for task-based and resting fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection comprising participants from 54 different studies in the OpenfMRI repository. We review the distinctive features of fMRIPrep in a qualitative comparison to other preprocessing workflows. We demonstrate that fMRIPrep achieves higher spatial accuracy as it introduces less uncontrolled spatial smoothness than one commonly used preprocessing tool. FMRIPrep has the potential to transform fMRI research by equipping neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow which can help ensure the validity of inference and the interpretability of their results.

The sparsity and bias of the Lasso selection in high-dimensional linear regression

Abstract: Meinshausen and Buhlmann [Ann. Statist. 34 (2006) 1436--1462] showed that, for neighborhood selection in Gaussian graphical models, under a neighborhood stability condition, the LASSO is consistent, even when the number of variables is of greater order than the sample size. Zhao and Yu [(2006) J. Machine Learning Research 7 2541--2567] formalized the neighborhood stability condition in the context of linear regression as a strong irrepresentable condition. That paper showed that under this condition, the LASSO selects exactly the set of nonzero regression coefficients, provided that these coefficients are bounded away from zero at a certain rate. In this paper, the regression coefficients outside an ideal model are assumed to be small, but not necessarily zero. Under a sparse Riesz condition on the correlation of design variables, we prove that the LASSO selects a model of the correct order of dimensionality, controls the bias of the selected model at a level determined by the contributions of small regression coefficients and threshold bias, and selects all coefficients of greater order than the bias of the selected model. Moreover, as a consequence of this rate consistency of the LASSO in model selection, it is proved that the sum of error squares for the mean response and the $\ell_{\alpha}$-loss for the regression coefficients converge at the best possible rates under the given conditions. An interesting aspect of our results is that the logarithm of the number of variables can be of the same order as the sample size for certain random dependent designs.

Rare and Emerging Opportunistic Fungal Pathogens: Concern for Resistance beyond Candida albicans and Aspergillus fumigatus

Abstract: The frequency of invasive mycoses due to opportunistic fungal pathogens has increased significantly over the past two decades ([35][1], [74][2], [83][3], [88][4], [89][5], [101][6], [106][7]). This increase in infections is associated with excessive morbidity and mortality ([33][8], [50][9], [108][