We randomly assigned frail elderly inpatients with a high probability of nursing-home placement to an innovative geriatric evaluation unit intended to provide improved diagnostic assessment, therapy, rehabilitation, and placement. Patients randomly assigned to the experimental (n = 63) and control (n = 60) groups were equivalent at entry. At one year, patients who had been assigned to the geriatric unit had much lower mortality than controls (23.8 vs. 48.3 per cent, P less than 0.005) and were less likely to have initially been discharged to a nursing home (12.7 vs. 30.0 per cent, P less than 0.05) or to have spent any time in nursing home during the follow-up period (26.9 vs. 46.7 per cent, P less than 0.05). The control-group patients had substantially more acute-care hospital days, nursing-home days, and acute-care hospital readmissions. Patients in the geriatric unit were significantly more likely to have improvement in functional status and morale than controls (P less than 0.05). Direct costs for institutional care were lower for the experimental group, especially after adjustment for survival. We conclude that geriatric evaluation units can provide substantial benefits at minimal cost for appropriate groups of elderly patients, over and above the benefits of traditional hospital approaches.

No abstract

Although unenhanced CT quantifies the degree of macrovesicular steatosis relatively well, it may preclude a liver biopsy only in a small percentage of potential donors with low LAI (unacceptable degree of steatosis). Core liver biopsy is still necessary in the majority of donors with normal LAI to identify those with both fatty liver and coexistent hemosiderin deposition or radiologically occult diffuse liver diseases.

There appears to be a narrow transition zone for hepatic vessels at 2-4 mm, beyond which the heat sink effect was seen consistently and substantial vascular injury was rare.

The purpose of this work is to develop patient-specific models for automatically detecting lung nodules in computed tomography (CT) images. It is motivated by significant developments in CT scanner technology and the burden that lung cancer screening and surveillance imposes on radiologists. We propose a new method that uses a patient's baseline image data to assist in the segmentation of subsequent images so that changes in size and/or shape of nodules can be measured automatically. The system uses a generic, a priori model to detect candidate nodules on the baseline scan of a previously unseen patient. A user then confirms or rejects nodule candidates to establish baseline results. For analysis of follow-up scans of that particular patient, a patient-specific model is derived from these baseline results. This model describes expected features (location, volume and shape) of previously segmented nodules so that the system can relocalize them automatically on follow-up. On the baseline scans of 17 subjects, a radiologist identified a total of 36 nodules, of which 31 (86%) were detected automatically by the system with an average of 11 false positives (FPs) per case. In follow-up scans 27 of the 31 nodules were still present and, using patient-specific models, 22 (81%) were correctly relocalized by the system. The system automatically detected 16 out of a possible 20 (80%) of new nodules on follow-up scans with ten FPs per case.