The prediction rule we describe accurately identifies the patients with community-acquired pneumonia who are at low risk for death and other adverse outcomes. This prediction rule may help physicians make more rational decisions about hospitalization for patients with pneumonia.

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The fatigue impact scale (FIS) was developed to improve our understanding of the effects of fatigue on quality of life. The FIS examines patients' perceptions of the functional limitations that fatigue has caused over the past month. FIS items reflect perceived impact on cognitive, physical, and psychosocial functioning. This study compared 145 patients referred for investigation of chronic fatigue (ChF) with 105 patients with multiple sclerosis (MS) and 34 patients with mild hypertension (HT). Internal consistency for the FIS and its three subscales was > .87 for all analyses. Fatigue impact was highest for the ChF group although the MS group's reported fatigue also exceeded that of the HT group. Discriminant function analysis correctly classified 80.0% of the ChF group and 78.1% of the MS group when these groups were compared. This initial validation study indicates that the FIS has considerable merit as a measure of patient's attribution of functional limitations to symptoms of fatigue.

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Context.-Many groups have developed guidelines to shorten hospital length of stay in pneumonia in order to decrease costs, but the length of time until a patient hospitalized with pneumonia becomes clinically stable has not been established. Objective.-To describe the time to resolution of abnormalities in vital signs, ability to eat, and mental status in patients with community-acquired pneumonia and assess clinical outcomes after achieving stability. Design.-Prospective, multicenter, observational cohort study. Setting.-Three university and 1 community teaching hospital in Boston, Mass,

Metabolomics may have the capacity to revolutionize disease diagnosis through the identification of scores of metabolites that vary during environmental, pathogenic, or toxicological insult. NMR spectroscopy has become one of the main tools for measuring these changes since an NMR spectrum can accurately identify metabolites and their concentrations. The predominant approach in analyzing NMR data has been through the technique of spectral binning. However, identification of spectral areas in an NMR spectrum is insufficient for diagnostic evaluation, since it is unknown whether areas of interest are strictly caused by metabolic changes or are simply artifacts. In this paper, we explore differences in gender, diurnal variation, and age in a human population. We use the example of gender differences to compare traditional spectral binning techniques (NMR spectral areas) to novel targeted profiling techniques (metabolites and their concentrations). We show that targeted profiling produces robust models, generates accurate metabolite concentration data, and provides data that can be used to help understand metabolic differences in a healthy population. Metabolites relating to mitochondrial energy metabolism were found to differentiate gender and age. Dietary components and some metabolites related to circadian rhythms were found to differentiate time of day urine collection. The mechanisms by which these differences arise will be key to the discovery of new diagnostic tests and new understandings of the mechanism of disease.