ABSTRACT
OBJECTIVES: The US Department of Veterans Affairs (VA) is the largest integrated health care provider for HIV-infected patients in the USA. VA data for HIV-specific clinical and quality improvement research are an important resource. We sought to determine the accuracy of using the VA Corporate Data Warehouse (CDW), a fully automated medical records database for all VA users nationally, to identify HIV-infected patients compared with a gold-standard VA HIV Clinical Case Registry (CCR). METHODS: We assessed the test performance characteristics of each of our CDW criteria-based algorithms (presence of one, two or all of the following: diagnostic codes for HIV, positive HIV laboratory tests, and prescription for HIV medication) by calculating their sensitivity (proportion of HIV-positive patients in the CCR accurately detected as HIV-positive by the CDW algorithm) and positive predictive value (PPV; the proportion of patients identified by the CDW algorithm who were classified as HIV-positive from the CCR). RESULTS: We found that using a CDW algorithm requiring two of three HIV diagnostic criteria yielded the highest sensitivity (95.2%) with very little trade-off in PPV (93.5%). CONCLUSIONS: A two diagnostic criteria-based algorithm can be utilized to accurately identify HIV-infected cohorts seen in the nationwide VA health care system.
Subject(s)
HIV Infections/diagnosis , HIV Infections/epidemiology , Algorithms , Cohort Studies , Delivery of Health Care, Integrated , Early Diagnosis , Electronic Health Records , Female , Humans , International Classification of Diseases , Male , Sensitivity and Specificity , United States/epidemiology , United States Department of Veterans AffairsABSTRACT
Observations of high-redshift supernovae indicate that the Universe is accelerating. Here we present a model-independent method for estimating the form of the potential V(phi) of the scalar field driving this acceleration, and the associated equation of state w(phi). Our method is based on a versatile analytical form for the luminosity distance D(L), optimized to fit observed distances to distant supernovae and differentiated to yield V(straight phi) and w(straight phi). Our results favor w(phi) approximately -1 at the present epoch, steadily increasing with redshift. A cosmological constant is consistent with our results.
Subject(s)
Astronomy , Cosmic Radiation , Astronomy/methods , Light , Physics/methodsABSTRACT
The quantitative morphological classification of galaxies is important for understanding the origin of type frequency and correlations with environment. However, galaxy morphological classification is still mainly done visually by dedicated individuals, in the spirit of Hubble's original scheme and its modifications. The rapid increase in data on galaxy images at low and high redshift calls for a re-examination of the classification schemes and for automatic methods. Here are shown results from a systematic comparison of the dispersion among human experts classifying a uniformly selected sample of more than 800 digitized galaxy images. These galaxy images were then classified by six of the authors independently. The human classifications are compared with each other and with an automatic classification by an artificial neural network, which replicates the classification by a human expert to the same degree of agreement as that between two human experts.
