William Laverty

Does antacid prophylaxis prevent upper gastrointestinal bleeding in critically ill patients

Sixty-five surgical ICU patients at high risk of developing acute erosive gastritis and bleeding received prophylactic antacid treatment to maintain a gastric pH of at least 5.0. A similar control group of 61 patients received no specific prophylaxis. All patients in both groups developed microscopic bleeding; however, microscopic bleeding did not influence outcome. In the control group, eight (13.1%) patients developed moderate visible bleeding, as compared to seven (10.8%) patients in the antacid group, an insignificant difference. A single patient in the control group developed severe GI bleeding due to acute erosive gastritis. Antacid prophylaxis did not prevent macroscopic bleeding and there was no correlation between the number of risk factors in individual patients and the rate of upper GI bleeding. We conclude that antacid is not required to prevent upper GI bleeding in high-risk critically ill patients.

Robust descriptive discriminant analysis for repeated measures data

Discriminant analysis (DA) procedures based on parsimonious mean and/or covariance structures have recently been proposed for repeated measures data. However, these procedures rest on the assumption of a multivariate normal distribution. This study examines repeated measures DA (RMDA) procedures based on maximum likelihood (ML) and coordinatewise trimming (CT) estimation methods and investigates bias and root mean square error (RMSE) in discriminant function coefficients (DFCs) using Monte Carlo techniques. Study parameters include population distribution, covariance structure, sample size, mean configuration, and number of repeated measurements. The results show that for ML estimation, bias in DFC estimates was usually largest when the data were normally distributed, but there was no consistent trend in RMSE. For non-normal distributions, the average bias of CT estimates for procedures that assume unstructured group means and structured covariances was at least 40% smaller than the values for corresponding procedures based on ML estimators. The average RMSE for the former procedures was at least 10% smaller than the average RMSE for the latter procedures, but only when the data were sampled from extremely skewed or heavy-tailed distributions. This finding was observed even when the covariance and mean structures of the RMDA procedure were mis-specified. The proposed robust procedures can be used to identify measurement occasions that make the largest contribution to group separation when the data are sampled from multivariate skewed or heavy-tailed distributions.

Developing a mapping from affective words to design parameters for affective design of apparel products

The development of an emotion-based (or affect-based) apparel design system has become an important issue nowadays due to the customer’s increased demand for apparel products not only in the aspect of function but also of aesthetics or affect/emotion. This paper presents a study on developing a mapping from affective words to design parameters. The technique employed to develop this mapping is neural networks (NNs). Both linear NNs and higher-order NNs were applied. An example was taken to illustrate and validate the developed mapping. There are two main contributions from the study. The first is that this mapping is the first in the domain of apparel design, and with it, the computer-aided affect-based design for apparel becomes possible. The second one is the provision of some empirical knowledge for the evaluation of so-called higher-order NNs.

The effects of password length and reference profile size on the performance of a multivariate text-dependent typist verification system

Abstract The performance of Napier et al.s typist verification algorithm (Keyboard user verification: toward an accurate, efficient, and ecologically valid algorithm, International Journal of Human-Computer Studies 43 (1995) 213-222) was assessed in a text-dependent setting. Twenty-nine subjects typed a 17 character password 50 times. False acceptance and false rejection rates were then calculated as the number of repetitions of the password included in the reference profile was increased from 6 to 20 and the number of digraphs from the password included in the verification process was increased from 2 to 16. The performance of the system (12% total error rate) was found to be comparable with the best results reported in other studies using text-dependent algorithms, and substantially better than that reported in studies using a text-independent paradigm with passwords of this length. The relationship between password length and reference profile size was found to conform to an exponential decay function, which accounted for 92% of the variability in verification error rates.

An Evaluation of the Long-Term Treatment Outcomes of an Interdisciplinary Chronic Pain Centre Program

Objectives The purpose of this study was to re-evaluate clients of a chronic pain center treatment program at least one year following their completion of the program to determine whether they had maintained improvements and to compare them to a group of clients who underwent the initial assessment but did not attend the treatment program. Methods The Chronic Pain Center, located in a mid-size Canadian city, offers an interdisciplinary treatment program. They had documented statistically significant improvement on several measures at the completion of the program but had no formal evaluation of longer-term effects. All clients assessed between 2004 and 2007 were invited to complete an evaluation by mail-out questionnaires, resulting in 142 participants [treatment group  =  88, comparison group  =  54]. Results The treatment group demonstrated significant improvement from assessment to discharge on all outcome measures. These improvements declined over time but remained significantly improved from the admission scores [Wilks’ Λ  =  0.501, F(1,48)  =  4.788, P  =  0.001]. However, no significant differences between the treatment and the comparison groups were found on any of the outcome measures at follow-up [Wilks’ Λ  =  0.930, F(1,107)  =  1.014, P  =  0.430]. Conclusions Statistically significant improvement of treatment participants, over a short time period, which was maintained over time, strengthens the inference that the treatment program had a positive impact. Study limitations, including recruitment method and the use of a non-randomized comparison group, may have affected the ability to demonstrate a difference between the treatment and the comparison groups. Statistically significant improvements, however, need to be studied in more depth to determine how they relate to clinical significance.

An Examination of Male and Female Monthly Employment Rates over Time in Canada and the United States Using Hidden Markov Probability Models

In this paper, we will illustrate the use and power of Hidden Markov models in analyzing multivariate data over time. The data used in this study was obtained from the Organization for Economic Co-operation and Development (OECD. Stat database url: https://stats.oecd.org/) and encompassed monthly data on the employment rate of males and females in Canada and the United States (aged 15 years and over; seasonally adjusted from January 1995 to July 2018). Two different underlying patterns of trends in employment over the 23 years observation period were uncovered.