Diagnostic performance of a new red light LED device for approximal caries detection

The aim of this study was to test a newly developed LED-based fluorescence device for approximal caries detection in vitro. We assembled 120 extracted molars without frank cavitations or fillings pairwise in order to create contact areas. The teeth were independently assessed by two examiners using visual caries detection (International Caries Detection and Assessment System, ICDAS), bitewing radiography (BW), laser fluorescence (LFpen), and LED fluorescence (Midwest Caries I.D., MW). The measurements were repeated at least 1 week later. The diagnostic performance was calculated with Bayesian analyses. Post-test probabilities were calculated in order to judge the diagnostic performance of combined methods. Reliability analyses were performed using kappa statistics for nominal data and intraclass correlation (ICC) for absolute data. Histology served as the gold standard. Sensitivities/specificities at the enamel threshold were 0.33/0.84 for ICDAS, 0.23/0.86 for BW, 0.47/0.78 for LFpen, and 0.32/0.87 for MW. Sensitivities/specificities at the dentine threshold were 0.04/0.89 for ICDAS, 0.27/0.94 for BW, 0.39/0.84 for LFpen, and 0.07/0.96 for MW. Reliability data were fair to moderate for MW and good for BW and LFpen. The combination of ICDAS and radiography yielded the best diagnostic performance (post-test probability of 0.73 at the dentine threshold). The newly developed LED device is not able to be recommended for approximal caries detection. There might be too much signal loss during signal transduction from the occlusal aspect to the proximal lesion site and the reverse.

An examination of the cross-cultural reliability and construct validity of a measure of loneliness

Loneliness is a pervasive, rather common experience in American culture, particularly notable among adolescents. However, the phenomenon is not well documented in the cross-cultural psychiatric literature. For psychiatric epidemiology to encompass a wide array of psychopathologic phenomena, it is important to develop useful measures to characterize and classify both non-clinical and clinical dysfunction in diverse subgroups and cultures.^ The goal of this research was to examine the cross-cultural reliability and construct validity of a scale designed to measure loneliness. The Roberts Loneliness Scale (RLS-8) was administered to 4,060 adolescents ages 10-19 years enrolled in high schools along either side of the Texas-Tamaulipas border region between the U.S. and Mexico. Data collected in 1988 from a study focusing on substance use and psychological distress among adolescents in these regions were used to examine the operating characteristics of the RLS-8. A sample stratified by nationality and language, age, gender, and grade was used for analysis.^ Results indicated that in general the RLS-8 has moderate reliability in the U.S. sample, but not in the Mexican sample. Validity analyses demonstrated that there was evidence for convergent validity of the RLS-8 in the U.S. sample, but none in the Mexican sample. Discriminant validity of the measures in neither sample could be established. Based on the factor structure of the RLS-8, two subscales were created and analyzed for construct validity. Evidence for convergent validity was established for both subscales in both national samples. However, the discriminant validity of the measure remains unsubstantiated in both national samples. Also, the dimensionality of the scale is unresolved.^ One primary goal for future cross-cultural research would be to develop and test better defined culture-specific models of loneliness within the two cultures. From such scientific endeavor, measures of loneliness can be developed or reconstructed to classify the phenomenon in the same manner across cultures. Since estimates of prevalence and incidence are contingent upon reliable and valid screening or diagnostic measures, this objective would serve as an important foundation for future psychiatric epidemiologic inquiry into loneliness. ^

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^

Assessing agglomeration economies in the Yangzi River Delta, China : a bayesian spatial econometric approach

This paper estimates the elasticity of labor productivity with respect to employment density, a widely used measure of the agglomeration effect, in the Yangtze River Delta, China. A spatial Durbin model is presented that makes explicit the influences of spatial dependence and endogeneity bias in a very simple way. Results of Bayesian estimation using the data of the year 2009 indicate that the productivity is influenced by factors correlated with density rather than density itself and that spatial spillovers of these factors of agglomeration play a significant role. They are consistent with the findings of Ke (2010) and Artis, et al. (2011) that suggest the importance of taking into account spatial dependence and hitherto omitted variables.

Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers

The main purpose of a gene interaction network is to map the relationships of the genes that are out of sight when a genomic study is tackled. DNA microarrays allow the measure of gene expression of thousands of genes at the same time. These data constitute the numeric seed for the induction of the gene networks. In this paper, we propose a new approach to build gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling. The interactions induced by the Bayesian classifiers are based both on the expression levels and on the phenotype information of the supervised variable. Feature selection and bootstrap resampling add reliability and robustness to the overall process removing the false positive findings. The consensus among all the induced models produces a hierarchy of dependences and, thus, of variables. Biologists can define the depth level of the model hierarchy so the set of interactions and genes involved can vary from a sparse to a dense set. Experimental results show how these networks perform well on classification tasks. The biological validation matches previous biological findings and opens new hypothesis for future studies

The diagnostic reliability of Internet-based observational kinematic gait analysis

We investigated the accuracy and reliability of observational kinematic gait assessments performed via a low-bandwidth Internet link (118 kbit/s) and a higher-speed Internet link (128 kbit/s). Twenty-four subjects were randomized to either bandwidth group. Gait was assessed with the Gait Assessment Rating Scale (GARS) in the traditional manner, which is from video-recordings, and with repeated measurements via the online method. Online assessment was found to provide as accurate a measure of gait performance as the traditional assessment (limits of agreement

WHOQOL-BREF as a measure of quality of life in older patients with depression

Objective: To assess the reliability and validity of a brief measure of quality of life recently developed by the World Health Organization, the WHOQOL-BREF, and to examine its association with a variety of clinical and sociodemographic factors in older depressed patients. Design: Cross-sectional study. Methods: Older depressed patients (N=41) underwent diagnostic assessment using the Composite International Diagnostic Interview (CIDI) and were independently assessed on a variety of measures including the WHOQOL-BREF (a 26-item self-report questionnaire generating four domain scores), Hamilton Depression Rating Scale (HAM-D); Geriatric Depression Scale (GDS); Mini-mental State Examination (MMSE); Modified Barthel Index (MBI); Instrumental activities of daily living (IADL), and measures of physical health status and social relationships. Estimates of inter-rater and test-retest reliability, and concurrent validity were made. Results: 39 subjects completed the study. The majority of subjects (94.9%) received a diagnosis of DSM-IV Major Depressive Disorder. Levels of comorbidity were high. Three of the four domains of the WHOQOL-BREF (Physical, Psychological and Environment domains) demonstrated satisfactory reliability and validity. However, the Social Relationships domain exhibited poor validity. Quality of life scores were strongly correlated with severity of depression, number of self-reported physical symptoms and self-assessed general health status. There was no relationship between diagnostic comorbidity and quality of life scores. Conclusions: The WHOQOL-BREF was successfully administered to older depressed patients although the concurrent validity of one of its four domains was poor. Quality of life scores were strongly correlated with severity of depression, raising the issue of measurement redundancy.

A parent-report measure of children's anxiety: psychometric properties and comparison with child-report in a clinic and normal sample

This study examined the psychometric properties of the parent version of the Spence Children's Anxiety Scale (SCAS-P); 484 parents of anxiety disordered children and 261 parents in a normal control group participated in the study. Results of confirmatory factor analysis provided support for six intercorrelated factors, that corresponded with the child self-report as well as with the classification of anxiety disorders by DSM-IV (namely separation anxiety, generalized anxiety, social phobia, panic/agoraphobia, obsessive-compulsive disorder, and fear of physical injuries). A post-hoc model in which generalized anxiety functioned as the higher order factor for the other five factors described the data equally well. The reliability of the subscales was satisfactory to excellent. Evidence was found for both convergent and divergent validity: the measure correlated well with the parent report for internalizing symptoms, and lower with externalizing symptoms. Parent-child agreement ranged from 0.41 to 0.66 in the anxiety-disordered group, and from 0.23 to 0.60 in the control group. The measure differentiated significantly between anxiety-disordered children versus controls, and also between the different anxiety disorders except GAD. The SCAS-P is recommended as a screening instrument for normal children and as a diagnostic instrument in clinical settings. (C) 2003 Elsevier Ltd. All rights reserved.

Validity of BMI as a measure of obesity in Australian white Caucasian and Australian Sri lankan children

Background: Body mass index ( BMI) is used to diagnose obesity. However, its ability to predict the percentage fat mass (% FM) reliably is doubtful. Therefore validity of BMI as a diagnostic tool of obesity is questioned. Aim: This study is focused on determining the ability of BMI- based cut- off values in diagnosing obesity among Australian children of white Caucasian and Sri Lankan origin. Subjects and methods: Height and weight was measured and BMI ( W/H-2) calculated. Total body water was determined by deuterium dilution technique and fat free mass and hence fat mass derived using age- and gender- specific constants. A % FM of 30% for girls and 20% for boys was considered as the criterion cut- off level for obesity. BMI- based obesity cut- offs described by the International Obesity Task Force ( IOTF), CDC/ NCHS centile charts and BMI- Z were validated against the criterion method. Results: There were 96 white Caucasian and 42 Sri Lankan children. Of the white Caucasians, 19 ( 36%) girls and 29 ( 66%) boys, and of the Sri Lankans 7 ( 46%) girls and 16 ( 63%) boys, were obese based on % FM. The FM and BMI were closely associated in both Caucasians ( r = 0.81, P < 0.001) and Sri Lankans ( r = 0.92, P< 0.001). Percentage FM and BMI also had a lower but significant association. Obesity cut- off values recommended by IOTF failed to detect a single case of obesity in either group. However, NCHS and BMI- Z cut- offs detected cases of obesity with low sensitivity. Conclusions: BMI is a poor indicator of percentage fat and the commonly used cut- off values were not sensitive enough to detect cases of childhood obesity in this study. In order to improve the diagnosis of obesity, either BMI cut- off values should be revised to increase the sensitivity or the possibility of using other indirect methods of estimating the % FM should be explored.

Motor unit number estimation - A Bayesian approach

All muscle contractions are dependent on the functioning of motor units. In diseases such as amyotrophic lateral sclerosis (ALS), progressive loss of motor units leads to gradual paralysis. A major difficulty in the search for a treatment for these diseases has been the lack of a reliable measure of disease progression. One possible measure would be an estimate of the number of surviving motor units. Despite over 30 years of motor unit number estimation (MUNE), all proposed methods have been met with practical and theoretical objections. Our aim is to develop a method of MUNE that overcomes these objections. We record the compound muscle action potential (CMAP) from a selected muscle in response to a graded electrical stimulation applied to the nerve. As the stimulus increases, the threshold of each motor unit is exceeded, and the size of the CMAP increases until a maximum response is obtained. However, the threshold potential required to excite an axon is not a precise value but fluctuates over a small range leading to probabilistic activation of motor units in response to a given stimulus. When the threshold ranges of motor units overlap, there may be alternation where the number of motor units that fire in response to the stimulus is variable. This means that increments in the value of the CMAP correspond to the firing of different combinations of motor units. At a fixed stimulus, variability in the CMAP, measured as variance, can be used to conduct MUNE using the "statistical" or the "Poisson" method. However, this method relies on the assumptions that the numbers of motor units that are firing probabilistically have the Poisson distribution and that all single motor unit action potentials (MUAP) have a fixed and identical size. These assumptions are not necessarily correct. We propose to develop a Bayesian statistical methodology to analyze electrophysiological data to provide an estimate of motor unit numbers. Our method of MUNE incorporates the variability of the threshold, the variability between and within single MUAPs, and baseline variability. Our model not only gives the most probable number of motor units but also provides information about both the population of units and individual units. We use Markov chain Monte Carlo to obtain information about the characteristics of individual motor units and about the population of motor units and the Bayesian information criterion for MUNE. We test our method of MUNE on three subjects. Our method provides a reproducible estimate for a patient with stable but severe ALS. In a serial study, we demonstrate a decline in the number of motor unit numbers with a patient with rapidly advancing disease. Finally, with our last patient, we show that our method has the capacity to estimate a larger number of motor units.

Bayesian invariant measurements of generalisation for discrete distributions

Neural network learning rules can be viewed as statistical estimators. They should be studied in Bayesian framework even if they are not Bayesian estimators. Generalisation should be measured by the divergence between the true distribution and the estimated distribution. Information divergences are invariant measurements of the divergence between two distributions. The posterior average information divergence is used to measure the generalisation ability of a network. The optimal estimators for multinomial distributions with Dirichlet priors are studied in detail. This confirms that the definition is compatible with intuition. The results also show that many commonly used methods can be put under this unified framework, by assume special priors and special divergences.

Evaluating the effectiveness of Bayesian feature selection

A practical Bayesian approach for inference in neural network models has been available for ten years, and yet it is not used frequently in medical applications. In this chapter we show how both regularisation and feature selection can bring significant benefits in diagnostic tasks through two case studies: heart arrhythmia classification based on ECG data and the prognosis of lupus. In the first of these, the number of variables was reduced by two thirds without significantly affecting performance, while in the second, only the Bayesian models had an acceptable accuracy. In both tasks, neural networks outperformed other pattern recognition approaches.

Is there a trade-off between quality and productivity? The case of diagnostic technologies in Portugal

The purpose of this paper is twofold: first, we compute quality-adjusted measures of productivity change for the three most important diagnostic technologies (i.e., the Computerised Tomography Scan, Electrocardiogram and Echocardiogram) in the major Portuguese hospitals. We use the Malmquist–Luenberger index, which allows to measure productivity growth while controlling for the quality of the production. Second, using non-parametric tests, we analyse whether the implementation of the Prospective Payment System may have had a positive impact on the movements of productivity over time. The results show that the PPS has helped hospitals to use these tools more efficiently and to improve their effectiveness.

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.