From bedside to bench: research agenda for frailty

The American Geriatrics Society sponsored a working conference in January 2004, funded by the National Institute on Aging, to establish the state of the art in frailty research and to set a research agenda for the future. The invited participants included senior basic biologists, epidemiologists, geneticists, and clinical investigators who study aging-related issues. This article summarizes the central theoretical observations on frailty and research needs and opportunities presented and discussed at this conference, and lays out an agenda for future research on frailty.     

The clubhouse model: An outcome study on attendance,work attainment and status,and hospitalization recidivism

The purpose of this outcome study is to assess the effectiveness of one clubhouse program model, Connections Clubhouse. The study was conducted on 117 randomly selected members attending Connections Clubhouse from January 1998 to June 1999. The study examines the effect of attendance rates on members' employment attainment and status, and on rate of hospitalization recidivism. Rate of hospitalization recidivism was conceptualized as the difference between number of hospitalizations after enrollment and before enrollment in the program. It is hypothesized that adult psychiatric patients, with a high rate of attendance at Connections Clubhouse, will demonstrate (a) a high rate of employment attainment and more advanced employment status; and (b) a low rate of hospitalization recidivism as compared to those with a low attendance rate. As hypothesized, the proportion of members with a high rate of attendance with no employment of any kind was significantly lower than the proportion of members with a low rate of attendance. Though less pronounced, the proportion of members with a high rate of attendance holding a more advanced employment status was significantly higher than the proportion of members with a low rate of attendance. As expected, the recidivism rate of members with a high rate of attendance was significantly lower than for members with a low rate of attendance.     

Semi-parametric analysis of multi-rater data

Datasets that are subjectively labeled by a number of experts are becoming more common in tasks such as biological text annotation where class definitions are necessarily somewhat subjective. Standard classification and regression models are not suited to multiple labels and typically a pre-processing step (normally assigning the majority class) is performed. We propose Bayesian models for classification and ordinal regression that naturally incorporate multiple expert opinions in defining predictive distributions. The models make use of Gaussian process priors, resulting in great flexibility and particular suitability to text based problems where the number of covariates can be far greater than the number of data instances. We show that using all labels rather than just the majority improves performance on a recent biological dataset.