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Journal of Clinical Microbiology, January 2004, p. 172-178, Vol. 42, No. 1
0095-1137/04/$08.00+0 DOI: 10.1128/JCM.42.1.172-178.2004
Copyright © 2004, American Society for Microbiology. All Rights Reserved.
Secretariat of the Scientific Committee, Administration of Control Policy, Federal Agency for the Safety of the Food Chain, B-1000 Brussels,1 Department of Animal Health, Institute of Tropical Medicine, B-2000 Antwerp,2 Section of Pathology, Department of Biocontrol, Veterinary and Agrochemical Research Centre, B-1180 Uccle,3 Department of Infectious and Parasitic Diseases, Virology, Epidemiology, and Viral Diseases, Faculty of Veterinary Medicine, University of Liège, B-4000 Liège, Belgium4
Received 6 June 2003/ Returned for modification 13 September 2003/ Accepted 5 October 2003
Reporting of clinically suspected cattle is currently the most common method for detecting cases of bovine spongiform encephalopathy (BSE). Improvement of clinical diagnosis and decision-making remains crucial. A comparison of clinical patterns, consisting of 25 signs, was made between all 30 BSE cases, confirmed in Belgium before October 2002, and 272 suspected cases that were subsequently determined to be histologically, immunohistochemically, and scrapie-associated-fiber negative. Seasonality in reporting suspected cases was observed, with more cases being reported during wintertime when animals were kept indoors. The median duration of illness was 30 days. The 10 most relevant signs of BSE were kicking in the milking parlor, hypersensitivity to touch and/or sound, head shyness, panic-stricken response, reluctance to enter in the milking parlor, abnormal ear movement or carriage, increased alertness behavior, reduced milk yield, teeth grinding, and temperament change. Ataxia did not appear to be a specific sign of BSE. A classification and regression tree was constructed by using the following four features: age of the animal, year of birth, number of relevant BSE signs noted, and number of clinical signs, typical for listeriosis, noted. The model had a sensitivity of 100% and a specificity of 85%. This approach allows the use of an interactive decision-support tool, based entirely on odds ratios, a statistic independent of disease prevalence.
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