PubMed · 16579412
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In diagnosing diseases in clinical practice, a combination of three clinical findings is often used to represent each disease. This is largely because it is often difficult or impractical to assess for all possible combinations of symptoms and abnormal exam findings that occur in any particular disease. For most diseases, diagnostic triads are based on empirical observations. In this study, we determined diagnostic triads for chronic diseases using data mining procedures. We also verified the combinations' validity as well as our procedure for determining them. We used symptoms and examination findings from 477 patients with chronic diseases, collected as part of a 35-year longitudinal study begun in 1968. For each patient there were 295 items from examinations in internal medicine, dermatology, ophthalmology, dentistry and blood tests. We judged each item to be either normal or abnormal, and restricted the analysis to the abnormal findings. To analyze such an exhaustive assortment, we used the data mining technique of association analysis. The analysis generated three clinical findings for each disease. Diseases were defined based on blood tests. Searching through all 295 items to find the three most useful clinical findings would be impractical on a commodity PC. However, by excluding normal items, we were able to sufficiently reduce the total number of combinations so as to make combinatorial analysis on a PC feasible. In addition to more accurate diagnoses, we believe our technique can identify those diagnostic data that are more cost effective in terms of time and other resources required for their collection.
Since 1998, federal policy has explicitly required the use of "evidence-based" prevention programs in schools. We review how this policy has been implemented through state recipients of the Safe and Drug Free Schools (SDFS) Program, and how other federal and private agencies have supported the policy by providing guidance about the scientific evidence for specific programs' effectiveness. We report data from a survey of SDFS state office directors, and we compare and contrast the most popular lists of effective programs. State offices supply the infrastructure for administering the SDFS Program, providing technical assistance to local school districts, monitoring the implementation of federal policy at the local level, and determining funding eligibility based on compliance. We found that states rely heavily on federal lists to determine whether school districts are meeting federal policy requirements, particularly the National Registry of Effective Programs and Practices (NREPP). Both SDFS and NREPP are changing, however, and the changes do not bode well for the transfer of prevention science to schools. Conclusions and recommendations are presented.
Estimates of terrestrial Broad Habitat cover for Wales from the Countryside Survey 2000 stratified sample field mapping programme in Britain are compared with the findings of a full census field mapping project, the Habitat Survey of Wales. The Countryside Survey sampling regime comprised a stratified random sample of 1 km squares [corrected] covering <0.5% of the land surface. Comparative assessment indicates that although few of the sample-derived estimates for individual Broad Habitats are within 30% of the full census survey results, relative extents accord with data from the complete census survey for all Broad Habitats apart from Arable & Horticultural. The accuracy of this estimate is improved when the national boundary of Wales is taken into account in the sample stratification scheme. It is suggested that cultural land-use differences between countries render cropland habitat extent less predictable from physical environmental parameters than semi-natural habitat extent. It is also shown that the precision of sample-derived cover estimates is influenced by habitat pattern: the error term associated with habitats of broadly equal extent is greater for those with the most clumped distributions.