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Biomedical subjects

M Rimac

Publications and source records attributed to M Rimac.

2 recordsLinked to original sources

Epidemiological investigation of school-related injuries in Koprivnica County, Croatia.

AIM: To assess the prevalence of injuries in elementary schools and determine specific risk groups of school-age children. METHODS: According to the 1991 census, there were 6,398 children between 7 and 14 years of age in the study area of the former Koprivnica district. During the 1992-1997 period, 354 children were injured in school. The registration of injured children was performed via structured questionnaires filled out at the emergency clinic and outpatient surgical clinic of the General Hospital in Koprivnica. The mechanism of accident and activities preceding it were categorized according to the Nordic Medico-Statistical Committee classification. Chi-square test was used to determine groups of school children at specific risk and a classification tree was made on the basis of minimum entropy values for age, sex, activity, and mechanism of injury. RESULTS: The highest injury rate of was recorded in 12-year-olds (21.7%). Upper extremities were most common site of injury (52.8%), whereas the most common type of injury was contusion (45.2%). The rate of head injuries was 3.2 times higher in younger (aged 7-10) children, whereas the rate of sports injuries was 3.5-fold higher in older (aged 11-14) children (p=0.001). Entropy classification revealed younger school-age children to be at the highest risk of contusion due to a blow from a ball, an object, or contact during sports activities. CONCLUSION: In Koprivnica County, most school-related injuries occurred during sport activities (42%) and play during recess (55%), with specific differences in age and sex.

Adolescent↗

[Prognosis for life expectancy using the inductive learning method].

The aim of this paper was to find out the possibility of life expectancy achievement (LEA) prognosis in open population by using epidemiological data, to improve it and to determine the differences between regions. We were using inductive learning software tool, ASSISTANT Professional, based on modified Quinlan's inductive learning method. Data from an epidemiological study of oil/fat consuming influence on diabetes incidence in different regions in Croatia, 1887 examinees, have been used. In spite of limited number of attributes that were available, an improvement in prognosis of LEA has been made by selecting the proper attributes and by changing the limiting values of attributes (values that change the meaning of attributes). We reached the absolute accuracy of 75.54%. Even after further pruning of decision tree that lowered this value, Assistant showed 13% better result than those of random selected outcome. Differences between regions were also established that could not been explained with attributes that were used. Expanding the list of attributes and analysis of their influence in particular region can make further improvement in prognosis of LEA.

Croatia↗