Objective:The aims of this descriptive, cross-sectional investigation were to evaluate the gingival health awareness of dental students by comparing their clinical gingival bleeding scores and self-reports, and to compare differences in awareness between males and females.Methods:In total, 100 (51 males, 49 females) freshman dental students were included in the study. Periodontal indices recorded were: Presence of plaque percentage (plaque index [PI], %), percentage of sites of bleeding on probing (BOP, %), probing depth, and community periodontal index (CPI). Percent agreement, kappa agreement, sensitivity, and specificity were calculated by comparing their self-reported gingival bleeding and BOP%.Results:The self-reports of gingival bleeding exhibited statistically significant correlations with BOP% in females (r = 0.42, P = 0.003). Female students showed a higher degree of awareness when kappa agreement, 0.23 (males: 0.16, females: 0.39), sensitivity, 48% (males: 42%, females: 51%), and specificity, 95% (males: 90%, females: 100%) were calculated. Although male dental students had higher PI and CPI scores, there was no significant difference by gender in the clinical measurements.Conclusions:According to our results, the validity of self-reported gingival bleeding was higher among dental students than in previous population-based studies. Female dental students showed a higher degree of awareness than males of their gingival health. Half of the included dental students could not differentiate whether they had gingival bleeding when there was actual bleeding. More emphasis should be given to the education of dental students regarding the relationship between gingival bleeding and active periodontal disease.
We present new methods to efficiently answer complex queries overbiomedical ontologies and databases considering the relevant partsof these knowledge resources, and to generate shortest explanationsto justify these answers. Both algorithms rely on the high-levelrepresentation and efficient solvers of Answer Set Programming. Weapply these algorithms to find answers and explanations to some complexqueries related to drug discovery, over PharmGKB, DrugBank, BioGrid, CTD and Sider.
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