Unusual systemic and nondental effects of maxillary expansion therapy: A comprehensive and updated review of literature.

In dental practice, pediatric patients with a wide range of manifestations of malocclusion viz. crossbite, dental crowding, and skeletal Class III due to transverse discrepancy of the abnormally constricted maxilla or palate are found. However, the dental management of such kind of malocclusion with narrow palate needs a meticulous dental evaluation. Appliances used for treating such malocclusion conditions may include Slow-Maxillary Expansion (SME) or Rapid Maxillary Expansion (RME) orthodontic or orthopedic appliances. Considerable success has been described in the literature using the maxillary/palatal expansion modality. Unusual systemic and nondental effects of expansion appliances therapy have been described in some scientific studies. However, no comprehensive review article has been published describing the nondental and systemic effects of RME/SME therapy in Pediatric Dentistry. Considering the above scenarios, the updated and comprehensive review of the relevant literature is necessary for health professionals. Dental professionals including pediatric dentists and orthodontists need to consider such untoward or unexpected effects of RME/SME treatment modality. Hence, the current comprehensive review article has been written with the aim to meticulously describe the relevant scientific literature about nondental/extraoral and systemic effects of RME/SME appliances.

Random tree algorithm to analyse the relation between type of traumatic dental injuries and its demographic and predisposing factors - A cross-sectional study.

Background and Aim: Traumatic dental injuries (TDIs) have become the public dental health problem worldwide in children and adolescents. These injuries are complex and multifactorial in aetiology. This study was done with the aim to analyse the association of 'type of TDI' with its demographic and various predisposing factors in children by an advanced statistical method of machine learning (ML) of artificial intelligence (AI). Materials and Methods: The present study's data were gathered by conducting the observational cross-sectional study among index age-groups 12 and 15 years children of randomly selected schools of different geographical regions. Structured interviews and dental examinations performed were done to record the variables of TDIs in self-constructed proforma. The gathered data were analysed by employing the random-tree model of machine learning algorithm of IBM SPSS Modeler version-18 software. Results: Molar-relationship (2.5), age (1.75), sex (1.5) and geographical region/area (~1.5) were the most important predictors (factors) for the determination of type of dental injury as shown by the random tree model, whereas clinical factors like overjet (0.75), lip-competence (0.5) and overbite (0.5) showed lesser importance in the determination of type of TDIs. Conclusion: Demographic factors (age, sex and geographical region) and one clinical factor (molar-relation) were found as the stronger factors for determining the type of traumatic dental injury in children.

Assessment of the environmental risk factors associated with traumatic dental injuries among WHO index-aged children and adolescents.

BACKGROUND: Traumatic dental injuries (TDIs) are becoming a worldwide phenomenon owing to their variable prevalence, and their etiology is considered multifactorial and complex in interactions among various risk factors. These are preventable if risk factors are analyzed and interpreted in all possible scientific manners. The objective of this study was to assess the various environmental risk factors (places of occurrence of TDI) among World Health Organization (WHO) index-aged children and adolescents (12 and 15 years). MATERIAL AND METHODS: This was the epidemiological cross-sectional study conducted in Aligarh City, Uttar Pradesh, India, in the year 2018, with a sample size of 1000. Multistage random sampling technique was employed. Study data were collected by doing dental examination and structured interview with self-prepared pro forma in ten randomly selected sampling sites (schools) from different regions of the city. Subsequently, data were entered in an Excel sheet and then analyzed with the Chi-square test using the Statistical Package for the Social Sciences (SPSS) software. RESULTS: Overall, school (63; 46.7%) was found to be the most prevalent environmental risk factor for TDI followed by home (37; 27.4%), roadside (10; 7.4%), and playground (9; 6.7%). Fifteen (11.1%) affected individuals were not able to recall the place of the TDI incident, while 0.7% (1) had TDI occurrence in other places. Among 12-year index-aged children, home (15; 34.1%) was the most common place of TDI in rural regions, while school (27; 67.5%) was the most frequent place of TDI in urban regions. Among 15-year index-aged adolescents, school (19; 59.4%) was found to be the most common place in rural regions, whereas school (9; 47.4%) was the most frequent place of TDI in urban regions. CONCLUSION: Environmental factors, especially school premises, infrastructure, and playing facilities, need to be upgraded and built with an emphasis on a safer environment for children. Teachers, parents, and school children should be educated and made aware of the preventive measures for dental injuries in schools and homes. Appropriate safety gears should be mandatory to use among children and adolescents during outdoor sports and recreational activities.

Comparison of Two Different Non-Radiographic Mixed Dentition Analysis

Objective: To evaluate the applicability of two different non-radiographic mixed dentition analysis in school going children of Aligarh district, India. Material and Methods: Mesiodistal dimension was measured on the dental casts of 120 school going children with electronic digital vernier caliper. The following inclusion criteria were adopted: presence of all fully erupted permanent teeth; no congenital craniofacial anomalies; no previous history of orthodontic treatment; and presence of intact dentition with no proximal caries, restoration, or age related attrition. Predicted values of canines and premolars were obtained from Moyer's at 75th percentile and Tanaka and Johnston mixed dentition analysis. Descriptive statistics were used to calculate the mean and standard deviation. Paired observations were compared by t­test. The level of significance was at p<0.001. Results: The mean difference between the predicted and the actual value of canines and premolars using Moyer's probability table at 75th percentile and Tanaka and Johnston regression equation were statistically significant. No significant differences were found between Moyer's analysis 75th percentile and Tanaka and Johnston mixed dentition analysis in both arch and sexes. Conclusion: These two non-radiographic mixed dentition analysis overestimated the mesiodistal width of canines and premolars.