0019 for the NaiveBayesUpdateable algorithm). An ensemble
of high-performing machine-learning classifiers did not yield a higher area under the receiver operating curve than its component classifiers. Dimensional reduction decreased the computational requirements for multiple classifiers, but did not adversely affect classification performance. Conclusions. Using historical data, machine-learning classifiers can predict which surgical cases selleck screening library should prompt a preoperative request for an APS consultation. Dimensional reduction improved computational efficiency and preserved predictive performance.”
“Case Description-Severe disease and death were identified in cattle exhibited at a state fair that were naturally infected with ovine herpesvirus type 2 (OvHV-2).
Clinical Findings-Most affected cattle had anorexia, signs of depression, diarrhea, fever, and respiratory distress ultimately leading to death. Mean duration of clinical signs prior to death was 6 days (range, 1 to 26 days). Mean number of days between apparent exposure and death was 71 days (range, 46 to 139 days).
Treatment and Outcomes-19 of 132 cattle cohoused in 1 barn
died of malignant catarrhal fever (MCF). The diagnosis of sheep-associated MCF was confirmed on the basis of results of an OvHV-2-specific FOR assay performed on tissue samples obtained from affected cattle. The disease was associated but not significantly with distance from the center of the barn and was not associated with Bindarit distance from the center of the sheep pens.
Clinical Relevance-Outbreaks of MCF in cattle are unusual, particularly in association with livestock exhibitions. Because the Torin 2 molecular weight clinical signs may be similar to those of some transboundary diseases, cases of MCF should be reported and investigated. Findings for this outbreak provided
evidence to suggest that fair boards and veterinarians should reexamine biosecurity recommendations for livestock exhibitions. (J Am Vet Med Assoc 2010;237:87-92)”
“Objective. To identify empirically derived cutoffs for mild, moderate, and severe pain in persons with multiple sclerosis (MS). Design. Cross-sectional survey. Setting. Community-based survey. Participants. Convenience sample of 236 individuals with MS and pain. Intervention. Not applicable. Main Outcome Measures. Zero to 10 Numeric Rating Scale for pain severity (both average and worst pain) and Brief Pain Inventory for pain interference. Results. The optimal classification scheme for average pain was 02 = mild, 35 = moderate, and 610 = severe. Alternatively, the optimal classification scheme for worst pain was 04 = mild, 57 = moderate, and 810 = severe. Conclusions. The present study furthers our ability to use empirically based cutoffs to inform the use of clinical guidelines for pain treatment as well as our understanding of the factors that might impact the cutoffs that are most appropriate for specific pain populations.