Use of Music and Voice Stimulus on Patients With Disorders of Consciousness

The objectives of this study were to check music and voice message influence on vital signs and facial expressions of patients with disorders of consciousness and to connect the existence of patient`s responses with the Glasgow Coma Scale or with the Ramsay Sedation Scale. The method was a single-blinded randomized controlled clinical trial with 30 patients, from two intensive care units, being divided into two groups (control and experimental). Their relatives recorded a voice message and chose a song according to the patient`s preference. The patients were submitted to three sessions for three consecutive days. Significant statistical alterations of the vital signs were noted during the message playback (oxygen saturation-Day 1 and Day 3; respiratory frequency-Day 3) and with facial expression, on Day 1, during both music and message. The conclusion was that the voice message was a stronger stimulus than the music.

A Comparison of Bayesian and Sampling Theory Inferences in a Probit Model

HE PROBIT MODEL IS A POPULAR DEVICE for explaining binary choice decisions in econometrics. It has been used to describe choices such as labor force participation, travel mode, home ownership, and type of education. These and many more examples can be found in papers by Amemiya (1981) and Maddala (1983). Given the contribution of economics towards explaining such choices, and given the nature of data that are collected, prior information on the relationship between a choice probability and several explanatory variables frequently exists. Bayesian inference is a convenient vehicle for including such prior information. Given the increasing popularity of Bayesian inference it is useful to ask whether inferences from a probit model are sensitive to a choice between Bayesian and sampling theory techniques. Of interest is the sensitivity of inference on coefficients, probabilities, and elasticities. We consider these issues in a model designed to explain choice between fixed and variable interest rate mortgages. Two Bayesian priors are employed: a uniform prior on the coefficients, designed to be noninformative for the coefficients, and an inequality restricted prior on the signs of the coefficients. We often know, a priori, whether increasing the value of a particular explanatory variable will have a positive or negative effect on a choice probability. This knowledge can be captured by using a prior probability density function (pdf) that is truncated to be positive or negative. Thus, three sets of results are compared:those from maximum likelihood (ML) estimation, those from Bayesian estimation with an unrestricted uniform prior on the coefficients, and those from Bayesian estimation with a uniform prior truncated to accommodate inequality restrictions on the coefficients.