AI Article Synopsis
- The study investigates the infection risk from spatter and aerosol generated during dental scaling and aims to build a prediction model for hemolytic bacteria levels.
- Samples from 80 patients across four dental clinics were analyzed, correlating bacterial counts in the air and on dentists' masks with clinical indicators, comparing outcomes between patients rinsing with sterile water and chlorhexidine.
- Results revealed a positive correlation between decayed teeth and total bacteria, with lower bacterial counts on masks for patients using chlorhexidine, providing a predictive model based on clinical indicators and rinsing method.
Article Abstract
Unlabelled: The aim of this study is to analyze the infection risk through spatter and aerosolization during scaling and to create a prediction model of the total number of hemolytic bacteria.
Material And Methods: Air samples were collected prior to patient's arrival and spatter and aerosol samples were collected during scaling procedure in 80 patients of 4 different dental clinics. The dentists calculated DI, CI, DMFT. Only patients with CI > = 1 were included. The bacteriological results (CFU/m3) were correlated with clinical indicators. Patients were divided into 2 groups: one that rinse with sterile water and the other with chlohexidine mouth rinse 0.1%.
Results And Conclusions: Medium size effect positive correlations were found between the number of decayed teeth and the total number of bacteria and the total number of hemolytic bacteria that grew on plate attached to the dentist's mask. The mean number of bacteria and the mean number of hemolytic bacteria that grew on plate attached to the dentist's mask were significantly lower in the group that rinse with chlorhexidine 0.1%, when compared to the group that rinsed with sterile water. When controlling for the total number of bacteria and the total number of hemolytic bacteria from air sampling, the total number of hemolytic bacteria that grew on the plate attached to the dentist's mask can be predicted by CI, group membership and DMFT score.
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